An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection

Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.

Convolutional neural network based methods have dominated object detection in recent years, which can be divided into the one-stage approach and the two-stage approach. In general, the two-stage approach (e.g., Faster R-CNN) achieves high accuracy, while the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, we propose a novel single-shot based detector, namely RefineDet++, which achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. The proposed RefineDet++ consists of two inter-connected modules: the anchor refinement module and the alignment detection module. Specifically, the former module aims to (1) filter out negative anchors to reduce search space for the subsequent classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes (1) the refined anchors as the input from the former module with (2) a newly designed alignment convolution operation to further improve the regression accuracy and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multi-task loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC and MS COCO demonstrate that RefineDet++ achieves state-of-the-art detection accuracy with high efficiency.

Object detection from UAV thermal infrared images and videos using YOLO models

In the recent generations, object detection (OD) has played an important role ranging from navigation of autonomous vehicles to video data compression. In the area of OD, deep learning (DL) has played a vital role in detecting the objects automatically. Breakthroughs have been achieved in the field of object detection with the simultaneous advancements in the field of DL. In this article, the author proposes a region-based neural network for detecting desired object in which it overcomes the computational complexity acquired by convolutional neural networks and provides a better accuracy. For training larger datasets and reusability, the deep learning principles are also proposed.

Deep Convolutional Neural Networks (CNN) are the state-of-the-art performers for the object detection task. It is well known that object detection requires more com- putation and memory than image classification. In this work, we propose LCDet, a fully-convolutional neural net- work for generic object detection that aims to work in em- bedded systems. We design and develop an end-to-end TensorFlow(TF)-based model. The detection works by a single forward pass through the network. Additionally, we employ 8-bit quantization on the learned weights. As a use case, we choose face detection and train the proposed model on images containing a varying number of faces of different sizes. We evaluate the face detection perfor- mance on publicly available dataset FDDB and Widerface. Our experimental results show that the proposed method achieves comparative accuracy comparing with state-of- the-art CNN-based face detection methods while reducing the model size by 3× and memory-BW by 3 - 4× compar- ing with one of the best real-time CNN-based object de- tector YOLO [23]. Our 8-bit fixed-point TF-model pro- vides additional 4× memory reduction while keeping the accuracy nearly as good as the floating point model and achieves 20× performance gain compared to the floating point model. Thus the proposed model is amenable for em- bedded implementations and is generic to be extended to any number of categories of objects.

Object detection has been a key task in computer vision with deep convolutional neural networks being a significant performer. We propose a method named Region Average Pooling that leverages object co-occurrence to improve object detection performance. Given regions of interest in an image, our method augments object detection networks with pooled contextual features from other regions of interest in the scene. We implement our scheme and evaluate it on the Pascal Visual Object Classes (VOC) 2007 and Microsoft Common Objects in Context (MS COCO) datasets. When used as part of the Faster R-CNN object detection framework with VGG-16, we show an increase in mAP from 24.2% to 25.5% over baseline Faster R-CNN and Global Average Pooling when testing on MS COCO.

Object detection is a core computer vision task that aims to localize and classify categories for various objects in an image. With the development of convolutional neural networks, deep learning methods have been widely used in the object detection task, achieving promising performance compared to traditional methods. However, designing a well-performing detection network is inefficient. It consumes too much hardware resources and time to trial, and it also heavily relies on expert knowledge. To efficiently design the neural network architecture, there has been a growing interest in automatically designing neural network architecture by Neural Architecture Search (NAS). In this paper, we propose a Memory-Efficient Multi-Agent Neural Architecture Search (MEMA-NAS) framework in end-to-end object detection neural network. Specifically, we introduce the multi-agent learning to search holistic architecture of the detection network. In this way, a lot of GPU memory is saved, allowing us to search each module’s architecture of the detection network simultaneously. To find a better tradeoff between the precision and computational costs, we add the resource constraint in our method. Search experiments on multiple datasets show that MEMA-NAS achieves state-of-the-art results in search efficiency and precision.

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.

Application of Deep Learning for Object Detection

Deep Learning Models or Neural Networks are loosely modeled based on the Human brain. Neural Networks consist of thousands of simple processing units that are densely interconnected, and are a means of performing machine learning. They aim to recognize patterns in data that enable a machine to perform some tasks based on analyzing several examples. One popular field of Deep Learning is Computer Vision that aims to simulate the complex visual system of human beings. An object detection model for instance, might be fed on thousands of images of objects to find visual patterns in the images that consistently correlate with particular labels. However, human visual perception is based on both visual and non-visual modalities of data. This paper aims to propose a Neural Network for Object detection that use various modalities of data, specifically Images and Text, to perform the object detection task

Object detection algorithms using convolutional neural networks (CNNs) achieve high detection accuracy, but it is challenging to realize real-time object detection due to their high computational complexity, especially on resource-constrained mobile platforms. In this article, we propose an algorithm-hardware co-optimization approach to designing a real-time object detection system. We first develop a compact object detection model based on a binarized neural network (BNN), which employs a new layer structure, the DenseToRes layer, to mitigate information loss due to deep quantization. We also propose an efficient object detection processor that runs object detection with high throughput using limited hardware rescources. We develop a resource-efficient processing unit supporting variable precision with minimal hardware overheads. Implemented in field-programmable gate array (FPGA), the object detection processor achieves 64.51 frames/s throughput with 64.92 mean average precision (mAP) accuracy. Compared to prior FPGA-based designs for object detection, our design achieves high throughput with competitive accuracy and lower hardware implementation costs.

The effectiveness of object detection largely depends on the availability of large annotated datasets to train the deep network successfully; however, obtaining a large-scale dataset is expensive and remains a challenge. In this work, we explore two different GAN-based approaches for data augmentation of agricultural images in a camouflaged environment. Camouflage is the property of an object which makes it hard to detect because of its similarity to its environment. We leverage paired and unpaired image-to-image translation to create synthetic images based on custom segmentation masks. We evaluate the quality of synthetic images by applying these to the object detection task as additional training samples. The experiments demonstrate that adversarial-based data augmentation significantly improves the accuracy of region-based convolutional neural network for object detection. Our findings show that when evaluated on the testing dataset, data augmentation achieves detection performance improvement of 3.97%. Given the difficulty of object detection task in camouflaged images, the result suggests that combining adversarial-based data augmentation with the original data can theoretically be synergistic in enhancing deep neural network efficiency to address the open problem of detecting objects in camouflaged environments.KeywordsGenerative adversarial networksData augmentationCamouflaged images

The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects.

Nowadays, Intelligent Transportation System (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to transport and traffic monitoring like vehicle segmentation and classification. Image understanding and object semantic segmentation in traffic scenes is a very important topic in the trend of autonomous driving issues, like autonomous vehicles and robotics. Actually, Deep Convolution Neural Network (CNN) architectures have given very good results and good performance in computer vision tasks such as classification, object detection, and segmentation. In this scope, we can distinguish two modes of CNN training, the pre-trained model and the training from scratch. In fact, deep neural network training without pre-trained weights and little data requires more training iterations. Besides, deeper models are more efficient than their shallow counterparts for the task of semantic segmentation. In this paper, we will make a comparative study between many deep CNNs models that can train the CNN model from scratch on small datasets, in addition, we will use data augmentation techniques. Extensive experiments were performed on the MiniCity urban scene dataset to prove the effectiveness of the used neural networks while obtaining promising results.

This paper proposes an enhancement of convolutional neural networks for object detection in resource-constrained robotics through a geometric input transformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrate execution times sixteen times quicker than the fastest competitor tested, while achieving outstanding accuracy.