Efficient Hardware Post Processing of Anchor-Based Object Detection on FPGA

Computer vision is nothing but a concept in which a computer or any other machine analyzes a scene like a human eye and performs some tasks accordingly. In the past few years, the research in the area of computer vision has increased on a very large scale due to its numerous applications in video analysis, scene detection, self - driving systems etc. The process of object detection is getting faster and better with development in the fields of neural networks and deep learning domains. In this paper, a comprehensive study is carried out on deep learning and its major component named convolution neural networks (CNN) that are largely used for object detection. At last paper shed lights on the various methods for object detection such as R-CNN, Fast R-CNN and Faster R-CNN and their applications in real world. The object detection applications has leads the world to digital revolution..

In modern convolutional neural network (CNN)-based object detector, the extracted features are not suitable for multi-scale detection and all the bounding boxes are simply ranked according to their classification scores in nonmaximum suppression (NMS). To address the above problems, we propose a novel one-stage detector named receptive field fusion RetinaNet. First, receptive field fusion module is proposed to extract richer multi-scale features by fusing feature maps of various receptive fields. Second, joint confidence guided NMS is proposed to optimize the post-processing process of object detection, which introduce location confidence in NMS and take joint confidence as the NMS rank basis. According to our experimental results, significant improvement in terms of mean of average precision (mAP) can be achieved on average compared with the state-of-the-art algorithm.

In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN’s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail.

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Camera traps are a popular tool to sample animal populations because they are noninvasive, detect a variety of species, and can record many thousands of animal detections per deployment. Cameras are typically set to take bursts of multiple images for each detection, and are deployed in arrays of dozens or hundreds of sites, often resulting in millions of images per study. The task of converting images to animal detection records from such large image collections is daunting, and made worse by situations that generate copious empty pictures from false triggers (e.g. camera malfunction or moving vegetation) or pictures of humans. We offer the first widely available computer vision tool for processing camera trap images. Our results show that the tool is accurate and results in substantial time savings for processing large image datasets, thus improving our ability to monitor wildlife across large scales with camera traps. In this dissertation, we have developed new image/video processing and computer vision algorithms for efficient and accurate object detection and sequence-level classiffication from natural scene camera-trap images. This work addresses the following five major tasks: (1) Human-animal detection. We develop a fast and accurate scheme for human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification. Specifically, first, We develop an effective background modeling and subtraction scheme to generate region proposals for the foreground objects. We then develop a cross-frame image patch verification to reduce the number of foreground object proposals. Finally, We perform complexity-accuracy analysis of deep convolutional neural networks (DCNN) to develop a fast deep learning classification scheme to classify these region proposals into three categories: human, animals, and background patches. The optimized DCNN is able to maintain high level of accuracy while reducing the computational complexity by 14 times. Our experimental results demonstrate that the proposed method outperforms existing methods on the camera-trap dataset. (2) Object segmentation from natural scene. We first design and train a fast DCNN for animal-human-background object classification, which is used to analyze the input image to generate multi-layer feature maps, representing the responses of different image regions to the animal-human-background classifier. From these feature maps, we construct the so-called deep objectness graph for accurate animal-human object segmentation with graph cut. The segmented object regions from each image in the sequence are then verfied and fused in the temporal domain using background modeling. Our experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods on the camera-trap dataset with highly cluttered natural scenes. (3) DCNN domain background modeling. We replaced the background model with a new more efficient deep learning based model. The input frames are segmented into regions through the deep objectness graph then the region boundaries of the input frames are multiplied by each other to obtain the regions of movement patches. We construct the background representation using the temporal information of the co-located patches. We propose to fuse the subtraction and foreground/background pixel classiffcation of two representation : a) chromaticity and b) deep pixel information. (4) Sequence-level object classiffcation. We proposed a new method for sequence-level video recognition with application to animal species recognition from camera trap images. First, using background modeling and cross-frame patch verification, we developed a scheme to generate candidate object regions or object proposals in the spatiotemporal domain. Second, we develop a dynamic programming optimization approach to identify the best temporal subset of object proposals. Third, we aggregate and fuse the features of these selected object proposals for efficient sequence-level animal species classification.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumi nation condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7recognition rate in person inde pendent evaluation over English, Bengali and Devanagari numerals, respectively. Object tracking is considered as an important task within the field of Computer Vision. The invention of faster computers, availability of inexpensive and good quality video cameras and demands of automated video analysis has given popularity to object tracking techniques. Generally, video analysis procedure has three major steps: firstly, detecting of the object, secondly tracking its movement from frame to frame and lastly analysing the behaviour of that object. For object tracking, four different issues are taken into account; selection of suitable object representation, feature selection for tracking, object detection and object tracking. In real world, Object tracking algorithms are the primarily part of different applications such as: automatic surveillance, video indexing and vehicle navigation etc. The generated text can also be used for various purposes, such as sending messages, emails, etc. It will be a powerful means of communication for the deaf. It is an effective communication method that reduces mobile and laptop usage by eliminating the need to write. Key Words: Air Writing, Character Recognition, Object Detection, Real-Time Gesture Control System, Computer Vision , Hand tracking.

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. After a picture is detected by the target, a large number of redundant anchors will be obtained, so we need to go through NMS to process these repeated anchors. Its essence is to extract the target detection frame with high confidence and suppress the false detection frame with low confidence. The biggest problem in the NMS algorithm is that it completely removes adjacent low-confidence detection frames. In some images with dense targets, this is very likely to cause missed detection and false detection. Therefore, we proposed the Simple-NMS algorithm, which adds two new thresholds to the original NMS, and sets a constraint condition different from the original based on the new threshold, and does not change the complexity of the traditional NMS algorithm, which can be very good Improve the effectiveness of NMS missed targets. The new NMS algorithm is improved on the standard data sets PASAL VOC2007 and MS COCO. In addition, the algorithm is simple and efficient, and can be easily integrated into any other object detection process.

In recent years, Convolutional Neural Networks (CNNs) have been widely successful for numerous computer vision tasks using 2D color and 3D point data, such as object detection and classification, and 3D pose estimation. Despite these successes, it comes with a price --- CNNs require a vast amount of labeled training data to be successful. Many training datasets are now available, however, they are mostly limited to general objects in the public domain such as people, cars, buildings, fruit, and more. Thus, not all fields have sufficient training data. Even if an adequate number of data samples can be built into a dataset, manually collecting and labeling the training data remains a laborious task. Several approaches to reduce or eradicate the need for training data have been proposed by researchers. The two most popular approaches are training with synthetic data and training on generalized object features. Training with synthetic data refers to the use of computer graphics to generate training data. While this approach has demonstrated success, it comes with its own challenges. In contrast, training a CNN on generalized object features identifies specific local 3D features or control points on objects. Local 3D features and their descriptors have proven in the past to be universally applicable and effective for general object detection, and thus, this method allows for a generalized CNN. A generalized CNN refers to a network architecture that, after initial training, can be specialized for a new task with a minimum amount or no new training data. This dissertation investigates an encoder-decoder architecture and determines the capabilities of it to generalize the CNN architecture for object detection tasks in point cloud data. A CNN is trained to recognize 3D features and demonstrates, without retraining, its successful applicability for point cloud object detection using disparate data. Additionally, the geometric consistency of the descriptors is evaluated to obtain further insight. The results demonstrate that the proposed 3D feature descriptors, and their increased geometric consistency contribute to the increased encoder-decoder architecture performance. In summary, this dissertation contributes a state-of-the-art, generalized, feature descriptor-based CNN architecture that can be transferred to different objects without retraining. Furthermore, it provides insight explaining the increased performance of the CNN compared to the state-of-the-art.

The convolution neural networks have revolutionized the computer vision domain. It has proven to be a dominant technology to carry out tasks such as image classification, semantic segmentation, and object detection. The convolution neural networks surpass the performance of the existing algorithms such as SIFT, HOG, etcetera. Where, instead of manually engineering the features, supervised learning help to learn the essential low-level and high-level features necessary for classifications. The convolution neural networks have become a popular tool to counter computer vision problems. However, it is computationally, and memory intensive to train and deploy the network because of the model size of a deep convolution neural networks. However, the research in the field of design space exploration (DSE) of neural networks and compression techniques to develop compact architectures, have made convolution neural networks memory and computationally efficient. These techniques have also improved the feasibility of convolution neural network for deployment on embedded targets. The paper explores the concept of compact convolution filters to reduce the number of parameters in a convolution neural network. The intuition behind the approach is that replacing convolution filters with a stack of compact convolution filters helps in developing a compact architecture with competitive accuracy. This paper explores the fire module a compact convolution filter and proposes a method of recreating a state-of-the-art architecture VGG-16 using the fire modules to develop a compact architecture, which is further trained on the CIFAR-10 dataset and deployed on a real-time embedded platform known as Bluebox 2.0 by NXP using RTMaps software framework.

Towards accurate dense pedestrian detection via occlusion-prediction aware label assignment and hierarchical-NMS

— In today's era, the challenge of object detection within laboratory spaces is paramount. The need for automated monitoring systems supporting object detection within laboratory settings, particularly for service robots, is crucial for scientific research and safety. Utilizing convolutional artificial neural networks has shown promising capabilities in object recognition. This paper proposes a novel method for detecting and localizing specific objects within laboratory environments using deep learning techniques. Employing a lightweight primary feature extractor allows for the identification of crucial object features without imposing excessive computational demands. Additionally, a depthwise separable convolution module is introduced, designed to capture essential components within multi-level convolutional patches specific to laboratory conditions. To address the challenges encountered within laboratory spaces, a new dataset is proposed, encompassing issues such as lighting variations, blurriness, occlusions, and complex backgrounds. Evaluation results indicate that the proposed model surpasses other lightweight object detection models in laboratory settings, offering high accuracy and efficiency. This model can be efficiently implemented on low-tier devices, ensuring real-time object detection and processing within laboratory environments. Keywords — object detection, service robot, convolutional neural networks, You Only Look Once version 5. Abstrak — Di era sekarang ini, tantangan deteksi objek di dalam ruang laboratorium menjadi sangat penting. Kebutuhan akan sistem pemantauan otomatis yang mendukung deteksi objek di dalam laboratorium, khususnya untuk robot servis, sangat penting untuk penelitian ilmiah dan keselamatan. Memanfaatkan jaringan saraf tiruan convolutional telah menunjukkan kemampuan yang menjanjikan dalam pengenalan objek. Makalah ini mengusulkan metode baru untuk mendeteksi dan melokalisasi objek tertentu di dalam lingkungan laboratorium menggunakan teknik pembelajaran mendalam. Menggunakan ekstraktor fitur utama yang ringan memungkinkan identifikasi fitur objek yang penting tanpa membebani komputasi yang berlebihan. Selain itu, modul konvolusi yang dapat dipisahkan secara mendalam juga diperkenalkan, yang dirancang untuk menangkap komponen penting dalam patch konvolusi multi-level yang spesifik untuk kondisi laboratorium. Untuk mengatasi tantangan yang dihadapi di dalam ruang laboratorium, dataset baru diusulkan, yang mencakup masalah seperti variasi pencahayaan, kekaburan, oklusi, dan latar belakang yang kompleks. Hasil evaluasi menunjukkan bahwa model yang diusulkan melampaui model pendeteksian objek ringan lainnya di lingkungan laboratorium, menawarkan akurasi dan efisiensi yang tinggi. Model ini dapat diimplementasikan secara efisien pada perangkat tingkat rendah, memastikan pendeteksian dan pemrosesan objek secara real-time di lingkungan laboratorium. Kata kunci — deteksi objek, robot layanan, jaringan saraf konvolusi, Anda Hanya Melihat Sekali versi 5.

Object detection is a major branch and fundamental task in computer vision, aiming to localize, identify and classify even the smallest objects of interest in images. Features can be extracted efficiently by deep convolutional neural networks (CNNs) as the backbone for real-time or near real-time object detection performance than the hand-crafted-based traditional methods. In the past few years, the advent of transformer-based models with robust self-attention mechanisms has not only raised object detection performance to a higher level but has also enabled it to produce excellent results. Many object detection tasks in the real world require that 3D information about the object be obtained, thus strengthening active research in 3D object detection. However, the algorithms for detecting 3D objects are not easy to propagate in real-world applications due to many factors, making reconstruction of 2D object detection algorithms to 3D object detection algorithms the suitable alternative. Therefore, we review the evolution of 2D object detection algorithms for digital imaging applications, focusing on their developments, models, applications, datasets, evaluation metrics, strengths and weaknesses, for better understanding of their landmarks and contributions to the advancement of the field.

Algorithms based on deep learning are taking off as solutions to several problems in numerous fields. Numerous deep learning architectures like convolutional neural networks, recurrent neural networks, deep belief networks, artificial neural networks and deep neural networks have been used in fields such as audio recognition, machine vision, processing of natural language, recognition of speech, social network filtering, computer vision, translating languages and bioinformatics. One such application of deep learning architectures is object detection. Object detection in satellite and aerial imagery undertakes an important role in a long list of applications and has allured a lot of attention which resulted in exceptional progress in recent years. It has abundant uses in various fields such as surveillance, military, national security, urban planning, environmental applications, traffic control and forestry. Several issues like varying size, background and orientation of the target object make the automatic detection of target in satellite imagery a challenging issue. Recent research in this field has given better speed and accuracy to the detection of the target object. With the rise in the data being generated by the Earth Observation programs, it is becoming much more imperative to find better and efficient ways to exploit this data by improving upon the existing Deep Learning research. In this paper, we bring forth a wide-ranging analysis of the recent advancements in methods of object detection based on deep learning to recognize the objects in satellite imagery.KeywordsDeep learningObject detectionSatellite imageryRemote sensingYou Only Look Once (YOLO)You Only Look Twice (YOLT)Single Shot Detector (SSD)Convolutional Neural Networks (CNN)Faster Region-based Convolutional Neural Networks (Faster RCNN)Hybrid Deep Convolutional Neural Network (HDNN)Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN)

Object detection is a crucial problem in computer vision that involves accurately locating objects in images or videos to identify instances to be detected. It has various applications, including facial detection, intelligent driving assistance, and satellite remote sensing detection. This review aims to aid researchers in quickly comprehending object detection. It covers the concept of object detection algorithms, analyzes the development history of object detection algorithms, and elaborates on the evolution process of object detection algorithms from independent development to combination with deep learning technology. The article divides object detection based on whether anchor boxes are generated during the detection process. It discusses the types of anchor boxes and non-anchor boxes and analyzes the current research status of single-stage and two-stage object detection algorithms. It also summarizes classic model structures in the development process of object detection algorithms and introduces difficulties in object detection. The article aims to solve the problem of object detection with a small number of samples and without detailed annotations during the training process. It summarizes and compares the advantages and disadvantages of various classic models, mainstream datasets, and evaluation indicators. Additionally, it looks forward to current challenges and future development directions in the field of object detection.