Recent Trends on Object Detection and Image Classification: A Review

Review on self-supervised image recognition using deep neural networks

Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.

There has been a breakthrough in the field of image classification and object detection, owing to the development of GPU and deep learning. However, because of the huge computation of deep learning, it is hard to use the deep learning algorithms in an embedded platform or a mobile device. Therefore, many compression studies have been conducted, and one of the most popular methods is a parameter quantization. In this paper, we propose an adaptive quantization scheme that reduces the loss of accuracy due to the quantization by properly mixing deterministic and stochastic quantization methods, while retaining the characteristics of the hardware-friendly fixed-point quantization method. By applying the proposed method to the weight parameters of image classification and object detection networks, the proposed method shows better mean average precision (mAP) of up to 0.44% in image classification and 0.91 % in object detection.

We propose Real Full Binary Neural Network (RFBNN), a method that can reduce the memory and compute power of Deep Neural Networks. This method has similar performance to other BNNs in image classification and object detection, while reducing computation power and memory size. In RFBNN, the weight filters are approximated as a binary value by applying a sign function; we apply real binary weight to the whole layer. Therefore, RFBNN can be efficiently implemented on CPU, FPGA and GPU. Results of the all experiments show that the proposed method works successfully on various task such as image classification and object detection. All layers in our networks are composed of only {1, −1}, and unlike the other BNN, there is no scaling factor. Compared to recent network binarization methods, BC, BWN and BWRN, we have reduced the memory size and computation costs, but the performance is the same or better.

Image classification is one of the important problems in the field of machine learning. Deep learning architectures are used in many machine learning applications such as image classification and object detection. The ability to manipulate large image clusters and implement them quickly makes deep learning a popular method in classifying images. This study points out the success of the convolutional neural networks which is the architecture of deep learning, in solving image classification problems. In the study, the convolutional neural network model of the winner of ilsvrc12 competition is implemented. The method distinguishes 1.2 million images with 1000 categories in success. The application is performed with the caffe library, and the image classification process is employed. In the application that uses the speed facility provided by GPU, the test operation is performed by using the images in Caltech-101 dataset.

CMOS Image sensors play a vital role in the exponentially growing field of Artificial Intelligence (AI). Applications like image classification, object detection and tracking are just some of the many problems now solved with the help of AI, and specifically deep learning. In this work, we target image classification to discern between six categories of fruits — fresh/ rotten apples, fresh/ rotten oranges, fresh/ rotten bananas. Using images captured from high speed CMOS sensors along with lightweight CNN architectures, we show the results on various edge platforms. Specifically, we show results using ON Semiconductor’s global-shutter based, 12MP, 90 frame per second image sensor (XGS-12), and ON Semiconductor’s 13 MP AR1335 image sensor feeding into MobileNetV2, implemented on NVIDIA Jetson platforms. In addition to using the data captured with these sensors, we utilize an open-source fruits dataset to increase the number of training images. For image classification, we train our model on approximately 30,000 RGB images from the six categories of fruits. The model achieves an accuracy of 97% on edge platforms using ON Semiconductor’s 13 MP camera with AR1335 sensor. In addition to the image classification model, work is currently in progress to improve the accuracy of object detection using SSD and SSDLite with MobileNetV2 as the feature extractor. In this paper, we show preliminary results on the object detection model for the same six categories of fruits.

Significant advancement of research on image classification and object detection has been achieved in the past decade.Deep convolutional neural networks have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit.However, learning a robust deep CNN model for object recognition is still quite challenging because image classification and object detection is a severely unbalanced large-scale problem.In this dissertation, we aim at improving the performance of image classification and object detection algorithms by taking advantage of deep convolutional neural networks by utilizing the following strategies: We introduce Deep Neural Pattern, a local feature densely extracted from an image with arbitrary resolution using a well trained deep convolutional neural network.We propose a latent CNN framework, which will automatically select the most discriminate region in the image to reduce the effect of irrelevant regions.We also develop a new combination scheme for multiple CNNs via Latent Model Ensemble to overcome the local minima problem of CNNs.In addition, a weakly supervised CNN framework, referred to as Multiple Instance Learning Convolutional Neural Networks is developed to alleviate strict label requirements.Finally, a novel residual-network architecture, Residual networks of Residual networks, is constructed to improve the optimization ability of very deep convolutional neural networks.All the proposed algorithms are validated by thorough experiments and have shown solid accuracy on large scale object detection and recognition benchmarks.

Purpose This study aims to automate the visual inspection of piling sheets in water channel construction using artificial intelligence (AI). By employing image classification and object detection techniques, the research focuses on extracting and analysing geometric features to enhance the accuracy and efficiency of the inspection process. It also addresses key challenges associated with the unique characteristics of construction materials and the limited variability of available inspection datasets. Design/methodology/approach Convolutional neural networks (CNNs) with varying complexities are employed for image classification, across four and six classes, and for object detection of piling sheets in water channel environments. A dataset provided by Witteveen + Bos is preprocessed to generate training sets, and the CNN architectures are optimized for enhanced performance. The accuracy and efficiency of the proposed models are evaluated and compared against traditional manual inspection methods. Findings The AI-driven approach significantly reduces processing time, evaluating 40,000 images in just 11.9 h, compared to approximately one month using manual assessment. The 4-class classification model achieves an accuracy of 96%, while the 6-class model attains 72%. The object detection model produces a mean average precision (mAP) of 79%. These results meet the performance standards set by the Dutch company Witteveen + Bos, which demonstrate the effectiveness of AI in automating the inspection of piling sheets. Originality/value This study introduces a novel AI-based approach for assessing piling sheets, demonstrating substantial improvements over traditional inspection methods. It introduces a systematic evaluation of various CNN architectures and hyperparameters to optimize the models specifically for piling sheet inspection rather than relying on off-the-shelf solutions. The use of CNNs for both image classification and object detection adheres to relevant Dutch engineering standards. Notably, the reduction in processing time, from one month to around 12 h, represents a major advancement in the efficiency of civil engineering inspections.

The objective of this paper is to build an Object Recognition model using ‘TINY YOLO’ and FPGA. Object Recognition is a CV (i. e., Computer Vision) technique for identifying and detecting objects in images or videos. Object recognition is a key output of DL (i. e., Deep Learning) and ML (i. e., Machine Learning). When we humans look at an image or video, we can readily spot people, object, scene and visual details. The aim of this is to teach a computer to do what comes innately to a human being. Object recognition is done with Image Classification, Object Localization and Object Detection that combines these two tasks and localizes and classifies an object in an image. Object Recognition has become a pivotal technology for driverless cars, security purpose, traffic surveillance etc., It is also used in a various application such as disease detection in bio imaging, industrial examination, and robotic vision. Hence, in a nutshell, ‘Object Recognition is the staple of automation industry’.

Residual networks (ResNets) represent a powerful type of convolutional neural network (CNN) architecture, widely adopted and used in various tasks. In this work we propose an improved version of ResNets. Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. We are able to show consistent improvements in accuracy and learning convergence over the baseline. For instance, on ImageNet dataset, using the ResNet with 50 layers, for top-1 accuracy we can report a 1.19% improvement over the baseline in one setting and around 2% boost in another. Importantly, these improvements are obtained without increasing the model complexity. Our proposed approach allows us to train extremely deep networks, while the baseline shows severe optimization issues. We report results on three tasks over six datasets: image classification (ImageNet, CIFAR-10 and CIFAR-100), object detection (COCO) and video action recognition (Kinetics-400 and Something-Something-v2). In the deep learning era, we establish a new milestone for the depth of a CNN. We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths. Code and models are publicly available at: https://github.com/iduta/iresnet.

This research study presents an innovative approach to food calorie estimation using deep learning and computer vision techniques. With the growing concern over diet-related health issues, the system offers a convenient solution for individuals to assess the nutritional value of their meals using smartphones or desktop computers. Key components of the system include image acquisition, object detection, segmentation, volume estimation, and calorie estimation. Users capture photos of their meals with standard reference objects, which are then processed using object detection algorithms like YOLO- V 4 Darknet to identify and localize food items. The proposed and widely used YOLO algorithm for object detection operates by first grid-dividing the input picture and then using the grid cells to estimate the bounding boxes and class probabilities. Subsequent segmentation techniques isolate the food items from the background, followed by volume estimation using geometric formulas. Calorie estimation is performed based on the estimated volume and density of the food items using pre-defined tables. Experimental results demonstrate the system's accuracy in estimating calorie content, making it a valuable tool for individuals aiming to make informed dietary choices and maintain a healthy lifestyle.

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.

Wheat is a primary carbohydrate-rich food commodity in the world, and the health of the wheat plants influences its production. Farmers can face a challenge due to decreased productivity caused by infected plants. However, wheat farmers have used conventional methods to survey diseases; however, these methods are often ineffective and inefficient, taking a lot of time and labor. And for enhanced wheat production, new strategies must be employed. Deep learning, specifically computer vision-based techniques, has proven significant capability in tasks like image classification, segmentation, and object detection. Deep learning techniques such as You Only Look Once (YOLO) models are state-of-the-art neural network algorithms used for accurate object detection. this study presents a comparative evaluation of two state-of-the-art object detection models, YOLOv5 and YOLOv8, for disease detection. Data augmentation techniques such as image noise, rotation, and flipping were implemented to improve the model’s performance during the training phase. The model’s performance was evaluated using metrics such as precision, recall, F1-score, and mean Average Precision (mAP).. The results show that the YOLOv5 and YOLOV8 models achieved good performance. They were able to detect the healthy and disease in images, These findings demonstrate that while both models are highly effective, but YOLOv8 offers greater robustness and accuracy for real-time detection.

Chapter 11 - Artificial intelligence for pathology