Object Detection Applications Research Articles

A comparison of deep learning-based object detection for unmanned aerial vehicle

Unmanned aerial vehicle (UAV) plays a critical role in the field of object detection, and machine learning techniques have significantly advanced the field in recent years. This paper provides a comprehensive overview of machine learning developments for UAV object detection. The process involves multiple steps. Firstly, deep learning and Convolutional Neural Network (CNN) are widely utilized to extract precise features and train object detection models for accurate classification and efficient recognition of target objects in images and videos. Secondly, classical object detection algorithms such as Faster R-CNN, You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD), have been enhanced to improve accuracy and real-time performance. In this review, we primarily focus on comparing the principles of CNN and YOLOv5 themselves, as well as their applications in object detection and image recognition. Ultimately, it becomes apparent that CNN is better suited for processing image data, automatically extracting features, and achieving more accurate classification and detection. On the other hand, YOLOv5 directly performs detection on large images, significantly reducing computation time compared to CNN.

Flexible and wearable battery-free backscatter wireless communication system for colour imaging

Wireless imaging, equipped with ultralow power wireless communications and energy harvesting (EH) capabilities, have emerged as battery-free and sustainable solutions. However, the challenge of implementing wireless colour imaging in wearable applications remains, primarily due to high power demands and the need to balance energy harvesting efficiency with device compactness. To address these issues, we propose a flexible and wearable battery-free backscatter wireless communication system specially designed for colour imaging. The system features a hybrid RF-solar EH array that efficiently harvests energy from both ambient RF and visible light energy, ensuring continuous operation in diverse environments. Moreover, flexible materials allow the working system to conform to the human body, ensuring comfort, user-friendliness, and safety. Furthermore, a compact design utilizing a shared-aperture antenna array for simultaneous wireless information and power transfer (SWIPT), coupled with an optically transparent stacked structure. This design not only optimizes space but also maintains the performance of both communication and EH processes. The proposed flexible and wearable systems for colour imaging would have potentially applications in environmental monitoring, object detection, and law enforcement recording. This approach demonstrates a sustainable and practical solution for the next generation of wearable, power-demanding devices.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

RGB-Angle-Wheel: A new data augmentation method for deep learning models

Deep learning models often rely on a diverse and well-augmented dataset for optimal performance. In this context, the methods of data augmentation are pivotal in boosting the models’ ability to generalize. In this paper, we introduce a novel data augmentation method, which we call RGB-Angle-Wheel, to improve the performance of deep learning models on RGB format images. This method involves rotating each color channel at specific angles to generate new training data that is distinct from the original dataset but shares similar properties. Experimental results on the CIFAR-10, CIFAR-100,and COCO datasets have validated the efficacy of the proposed method for enhancing model performance. Specifically, certain transformations in the red (R) and blue (B) channels improve model accuracy significantly, whereas the effect on the green (G) channel remains limited. These results indicate that the careful selection of transformation parameters plays a critical role in enhancing model performance. The findings of the study indicate that the proposed method can be utilized specifically for image processing, image classification, object detection, and other deep learning applications. Experiments demonstrate that the proposed method improves the model’s efficacy and generalizability.

Neural Radiance Fields (NeRF) for 3D Visualization Rendering Based on 2D Images

We are living in a 3D world, but oftentimes we visualize things in 2D scopes like in pictures and videos of different objects. The world of 3D visualization has been constantly evolving over the past few years, still growing with the introduction of cutting-edge computer vision and deep learning methods. Reconstructing 3D scenes and being able to interact with them in virtual environments used to be something beyond our capabilities but now with the use of Neural Radiance Fields (NeRF) and Instant Neural Graphic Primitives (InstantNGP) we are able to create a world in a virtual space, opening the gateways to applications in object detection and generation, augmented reality, medical imaging, and more. Access to these methods will allow for more immersive development by creators, hands-on educational resources, and more experimentation and research, stimulating future applications and techniques.

Sparse Embedded Convolution Based Dual Feature Aggregation 3D Object Detection Network

The algorithm design of compatible detection speed and accuracy based on LiDAR point clouds is a challenging issue in various practical applications of 3D object detection, including the field of autonomous driving. This paper designs a single-stage object detection algorithm that is lightweight and compatible with detection speed and accuracy for the above issue. To achieve these objectives, we propose a framework for a 3D object detection algorithm using a single-stage detection network as the backbone network. Firstly, we design a dual feature extraction module to reduce the occurrence of vehicle miss and error detection problems. Then, we use a multi-scale feature fusion scheme to fuse feature information with different scales. Furthermore, we design a data enhancement scheme suitable for this network architecture. Experimental results in the KITTI dataset show that the proposed method achieves improvement ratios of 38.5% for the detection speed and 2.88% ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document} 13.65% in terms of the average precision of vehicle detection compared to the existing algorithm based on single-stage object detection (SECOND).

Few-shot concealed object detection in sub-THz security images using improved pseudo-annotations

In this research, we explore the few-shot object detection application for identifying concealed objects in sub-terahertz security images, using fine-tuning based frameworks. To adapt these machine learning frameworks for the (sub-)terahertz domain, we propose an innovative pseudo-annotation method to augment the object detector by sourcing high-quality training samples from unlabeled images. This approach employs multiple one-class detectors coupled with a fine-grained classifier, trained on supporting thermal-infrared images, to prevent overfitting. Consequently, our approach enhances the model’s ability to detect challenging objects (e.g., 3D-printed guns and ceramic knives) when few-shot training examples are available, especially in the real-world scenario where images of concealed dangerous items are scarce.

Real time object tracking using deep learning and OpenCV

This article summarizes the background and application fields of Open-Source Computer Vision Library (OpenCV) and deep learning and conducts research based on their object detection and tracking applications. The model algorithm selected in this article is the Convolutional Neural Networks (CNN) algorithm. CNN algorithm can be used for object detection in real-time scenarios. Moreover, the CNN algorithm has good credibility. The Python program developed based on CNN algorithm can effectively achieve real-time object tracking. The model shows good detection and tracking performance for trained targets and can be further applied in more specific scenarios in the future. Because deep learning can process large-scale data and recognize complex patterns, it can automatically learn and extract advanced features. Combining the two can achieve faster and more accurate detection and tracking of target objects. After having a large enough training sample size, it can detect and track the specified object more accurately. However, it is precisely due to the enormous sample size required for deep learning that there are still some difficulties in applying it to real-time object tracking. This article first discusses the use of supervised learning methods for deep understanding. When the input sample capacity is large enough, the machine can better reflect the real-time detection and tracking of the target object. But the time required will significantly increase. This issue still needs to be addressed in the subsequent research process.

Visual Relationship Detection for Workplace Safety Applications

Applications of object and visual relationship detection for safety and security applications are in its infancy. State-of-the-art computer vision research is largely focused on improving mean Average Precision and mean Average Recall performance on standard, general datasets such as the Verbs in COmmon objects in COntext and the Visual Genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the back-end model for a decision support system. We use a naive Bayesian Network to determine scenarios where our proposed object and visual relationship detection network is error-prone. We also release a graphical user interface which demonstrates how our back-end neural network and naive Bayesian Network can be used for hazardous workplace safety and security applications. All models and source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/th-truong/rd-gui</uri> for future research and applications of object and visual relationship detection models in the safety and security sector.

Detection of Traffic Control Devices during Bridge Construction Projects Using Deep Learning

Recent days during the time of road construction so many accidents may occur due to the objects in the construction area cannot capture during travel. Some method must be implemented to classify and locate instances in the image. In deep learning area it extends its application to this field for object detection. Even though a method is identified it has risk factors such as real-time detection, changeable weather, and complex lighting conditions. In this paper we are discussing the algorithm used for detection and a next version of that algorithm to detect the object with more precision. And also the number frames capture during driving is also more than other algorithm. Here we discussed about YOLO algorithm, and which is the best algorithm that applies different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. The idea in YOLO v7 is to avoid that there is an identity connection when a convolutional layer with residual or concatenation is replaced by re-parameterized convolution.

Retracted: Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Retracted: Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach

Serverless edge systems simplify the deployment of real-time AI-based Internet of Things (IoT) applications at the edge. However, the heterogeneity of edge computing nodes – in terms of both hardware and software – makes load balancing challenging in these systems. In this paper, we propose a performance-driven, empirical weight-tuning approach to achieve effective load balancing based on the characteristics and capabilities of the nodes. By extensively profiling the nodes, we gather knowledge on performance metrics such as throughput, energy efficiency, response time, AI accuracy, and cost. Using this acquired knowledge, we introduce a weighted round-robin strategy to optimize the performance metrics according to their observed significance. To address multiple objectives, we introduce a multi-objective method that aims to strike a balance between any arbitrary set of performance objectives simultaneously. Additionally, we explore a coordinated distributed approach to overcome the limitations of centralized load balancing. Next, we introduce Hedgi, a heterogeneous serverless edge architecture designed to efficiently configure and utilize the derived load balancing policies, validated empirically. To demonstrate the practicality of Hedgi, we containerize and serverlessize a real-time object detection application. Extensive empirical studies are conducted using Hedgi to evaluate the performance of the proposed load balancing approach. The results provide valuable insights into the design trade-offs of various load balancing policies and system designs in the heterogeneous serverless edge.

Application of object detection and action recognition toward automated recognition of chemical experiments

Deep learning-based computer vision techniques enable object detection of chemical apparatus in images and action recognition of chemical experiment manipulations in videos.

YOLOv8 with Multi Strategy Integrated Optimization and Application in Object Detection

YOLOv8 with Multi Strategy Integrated Optimization and Application in Object Detection

Global-Sampling Spatial-Attention Module and its Application in Image Classification and Small Object Detection and Recognition

Global-Sampling Spatial-Attention Module and its Application in Image Classification and Small Object Detection and Recognition

From Detection to Action: Implementing Deep Learning Inference in PLC Systems via Docker Services

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment.

SODRet: Instance retrieval using salient object detection for self-service shopping

Self-service shopping technologies have become commonplace in modern society. Although various innovative solutions have been adopted, there is still a gap in providing efficient services to consumers. Recent developments in mobile application technologies and internet-of-things devices promote information and knowledge dissemination by integrating innovative services to meet users’ needs. We argue that object retrieval applications can be used to provide effective online or self-service shopping. Therefore, to fill this technological void, this study aims to propose an object retrieval system using a fusion-based salient object detection (SOD) method. The SOD has attracted significant attention, and recently many heuristic computational models have been developed for object detection. It has been widely used in object detection and retrieval applications. This work proposes an instance retrieval system based on the SOD to find the objects from the commodity datasets. A prediction about the object’s position is made using the saliency detection system through a saliency model, and the proposed SOD-based retrieval (SODRet) framework uses saliency maps for retrieving the searched items. The method proposed in this work is evaluated on INSTRE and Flickr32 datasets. Our proposed work outperforms state-of-the-art object retrieval methods and can further be employed for large-scale self-service shopping-based points of sales.

Synthetic Data for Object Detection with Neural Networks: State of the Art Survey of Domain Randomisation Techniques

Machine learning relies heavily on access to large and well-maintained datasets. In this article, we focus on Computer Vision and object detection applications to survey past research on automatic generation of annotated datasets that does not require costly and time-consuming human labelling. In specific, we analyse research done in the area of Domain Randomisation applied to Neural Networks predominant in object detection since the last decade. We propose a set of criteria for comparison of previously published works, and utilise these criteria to make conclusions about various trends in the area, similarities/differences and key discoveries made since conception. The purpose of this work is to advise practitioner on leading solutions and help researchers gain better understanding of the landscape. The key takeaways from our analysis show the current state of the art solutions within the mid-quartile range allow object detection with typically about 1-25% performance decrease in comparison to manually annotated datasets; while the top performant approaches above the upper quartile gain about 2-32% lead over real data training in their specific application areas. Our survey shows the future outlook is more research into 3D generation techniques, with most innovative yet complex techniques related to end-to-end modifications of entire network architectures to suit synthetic data training.

Synthetic Multimode Orbital Angular Momentum Generation and Its Object Detection Application

Synthetic Multimode Orbital Angular Momentum Generation and Its Object Detection Application

A mixed mutation strategy genetic algorithm for the effective training and design of optical neural networks

The optical neural networks (ONNs) have received lots of attention due to their superior speed and energy efficiency in large-scale linear matrix operations compared to electrical neural networks. Undoubtedly, the training and designing weights and other hyperparameters of ONNs to enhance performance is a pivotal concern. We present a mixed mutation strategy genetic algorithm (M2SGA) to deal with the problem of inefficient training. By combining the single-point mutation (SM), the uniform mutation (UM), and the Gaussian mutation (GM) operators, our method can achieve faster convergence speed and better robustness for the parameter training of ONNs than the standard genetic algorithm (SGA). Furthermore, an N-layer feedforward optical neural network based on Mach-Zehnder interferometers (MZIs) and electro-optic modulators (EOMs) is constructed to verify the feasibility of our scheme. And, four datasets are used to further confirm the effect of our proposed scheme on the classification tasks. Additionally, the impact of alternative operators on the performance of the algorithm is evaluated. Experimental results demonstrate that M2SGA exhibits higher accuracy and lower mean squared error compared with SGA. Consequently, M2SGA holds tremendous potential for applications in object detection, image processing and other related fields thanks to its great effect in datasets classification.