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An efficient automobile assembly state monitoring system based on channel-pruned yolov4 algorithm

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第3期37卷 372-382页

作者： Jiang, Daqi Wang, Hong Lu, Yanzheng Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

An efficient automobile assembly state monitoring system in industrial environment is presented in this paper. The system only needs to input a video that contains the whole detected parts and manually label it in the first frame. By finding the best point for tracking and tracking the point, the dataset can be automatically generated which saves time spent on manufacturing the dataset and makes the assembly state monitoring system easy to deploy into a practical industrial environment. The target detection algorithm uses the channel-pruned yolov4 neural network. The experimental result shows the algorithm balances speed and accuracy. Compared to original yolov4, the proposed method is two times faster and the performance is nearly equal to it. Comparative experiments show that the proposed algorithm performs better and is faster than other lightweight models which demonstrates that the channel pruning process dynamically improves the speed of the forward propagation without sacrificing accuracy. Additionally, the algorithms are deployed on two common embedded systems. The results show that in the industrial environment, the speed can fully meet real-time requirements.

关键词： Assembly state monitoring yolov4 algorithm channel pruning algorithm real-time detection

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yolov4 algorithm for the real-time detection of fire and personal protective equipments at construction sites

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第16期81卷 22163-22183页

作者： Kumar, Saurav Gupta, Himanshu Yadav, Drishti Ansari, Irshad Ahmad Verma, Om Prakash Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Instrumentat & Control Engn Jalandhar Punjab India Tech Univ Wien Fac Informat A-1040 Vienna Austria PDPM Indian Inst Informat Technol Dept Elect & Commun Engn Design & Mfg Jabalpur India

Many difficulties are encountered during evacuation from construction sites in hazardous situations, which may lead to severe fatalities. These fatalities, especially caused by fire, may be significantly reduced by ensuring personal protective equipment (PPE) compliance of construction site workers and fire detection through proper surveillance. Thus, the detection of PPEs, fire and injured or trapped persons, can greatly assist in the reduction of fatalities and economic loss. This article presents a novel approach towards the detection of fire and PPEs to assist in the monitoring and evacuation tasks. This work utilizes the yolov4 and yolov4-tiny algorithms based on deep learning for carrying out the detection task. A self-made dataset has been utilized to train the model in the Darknet neural network framework. Moreover, a comparative analysis with previous works has been carried out in order to endorse the real-time efficacy of the proposed work. The results verify the strength of yolov4 algorithm in real-time detection and surveillance at construction sites with maximum mean average precision (mAP) of 76.86 %.

关键词： CNN Fire Object detection PPE Surveillance yolov4 algorithm

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APPLICATION OF OPTIMISED yolov4 algorithm AND MULTI-SOURCE INFORMATION FUSION IN BLIND ZONE WARNING AND DETECTION OF CAR A-PILLARS

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MECHATRONIC SYSTEMS AND CONTROL 2025年第2期53卷 108-118页

作者： Wei, Xiaogang Chongqing Vocat Inst Tourism Dept Intelligent Mfg & Tourism Transportat Chongqing 409000 Peoples R China

With the continuous increase in the number of cars, the A-pillar blind spot has also become a safety hazard on the road. To better address this issue, a vehicle A-pillar blind spot warning and detection model based on object detection algorithm and multi-source information fusion is proposed. Firstly, vehicle detection is achieved through object detection algorithms to extract vehicle location information. Then, the multi-source information fusion technology is adopted, including cameras, sensors, etc., to obtain information about the surrounding environment of the vehicle. Finally, the performance verification is conducted through simulation experiments. The results show that the proposed model performs better than comparison methods in terms of object detection rate, error rate, recognition accuracy, and recall rate for different objects in A-pillar blind spot in the construction dataset. This verifies the reliability and stability of the proposed model in detecting A-pillar blind spot. The study provides an effective solution to the safety issues caused by blind spots in car A-pillars.

关键词： yolov4 algorithm multi-source information fusion vehicle A-pillar blind spots early warning detection

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Application of yolov4 algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment

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SENSORS 2022年第18期22卷 6851-6851页

作者： Chen, Yiming Sun, Xu Xu, Liang Ma, Sencai Li, Jun Pang, Yusong Cheng, Gang Shandong Zhongheng Optoelect Technol Co Ltd Zaozhuang 277000 Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Delft Univ Technol Fac Mech Maritime & Mat Engn NL-2628 Delft Netherlands

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the yolov4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective.

关键词： belt conveyor machine vision KinD plus plus algorithm yolov4 algorithm low-light enhancement

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Study on the Evaluation Method of Sound Phase Cloud Maps Based on an Improved yolov4 algorithm

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SENSORS 2020年第15期20卷 4314.-4314页

作者： Zhu, Qinfeng Zheng, Huifeng Wang, Yuebing Cao, Yonggang Guo, Shixu China Jiliang Univ Key Lab Acoust Res Hangzhou 310018 Peoples R China

Most sound imaging instruments are currently used as measurement tools which can provide quantitative data, however, a uniform method to directly and comprehensively evaluate the results of combining acoustic and optical images is not available. Therefore, in this study, we define a localization error index for sound imaging instruments, and propose an acoustic phase cloud map evaluation method based on an improved yolov4 algorithm to directly and objectively evaluate the sound source localization results of a sound imaging instrument. The evaluation method begins with the image augmentation of acoustic phase cloud maps obtained from the different tests of a sound imaging instrument to produce the dataset required for training the convolutional network. Subsequently, we combine DenseNet with existing clustering algorithms to improve the yolov4 algorithm to train the neural network for easier feature extraction. The trained neural network is then used to localize the target sound source and its pseudo-color map in the acoustic phase cloud map to obtain a pixel-level localization error. Finally, a standard chessboard grid is used to obtain the proportional relationship between the size of the acoustic phase cloud map and the actual physical space distance;then, the true lateral and longitudinal positioning error of sound imaging instrument can be obtained. Experimental results show that the mean average precision of the improved yolov4 algorithm in acoustic phase cloud map detection is 96.3%, the F1-score is 95.2%, and detection speed is up to 34.6 fps. The improved algorithm can rapidly and accurately determine the positioning error of sound imaging instrument, which can be used to analyze and evaluate the positioning performance of sound imaging instrument.

关键词： yolov4 algorithm sound source localization sound imaging instrument positioning error DenseNet k-medians plus plus

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Optimizing the visual effects of advertisement design using image analysis technology based on the yolov4 target detection algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Du, Yifeng Guangdong Polytech Sci & Technol Dept Tourism South Side Zhuhai Ave Zhuhai 519000 Guangdong Peoples R China

The lack of efficient quantitative analysis methods for visual elements in advertisement design often results in suboptimal visual impact and ineffective communication of key information. To address this challenge, this study employs image analysis technology based on the yolov4 (You Only Look Once v4) object detection algorithm to enhance the visual effectiveness of advertisement design. First, a diverse set of advertisement samples is collected, and image augmentation techniques are applied to expand the dataset. The yolov4 model is then trained using the CSPDarknet53 (Cross-Stage Partial Darknet53) network for feature extraction, integrating multi-scale prediction to improve detection accuracy. The trained model is subsequently utilized to identify and analyze visual elements within advertisements, assessing their spatial distribution and layout characteristics. Based on these insights, an automated optimization framework is developed to refine design elements such as color, size, and layout proportions, with the results visualized for comparative analysis. Experimental results demonstrate that the proposed method achieves a detection precision of 98.1% and a recall rate of 96.8% for product elements. The optimized print advertisements exhibit a focus concentration of 0.84 and an element distribution density of 0.87, significantly enhancing design clarity and effectiveness. This approach not only improves the efficiency and precision of quantitative analysis in advertisement design but also offers a practical and scalable solution for optimizing visual communication strategies.

关键词： Target detection visual layout optimization advertisement design analysis yolov4 algorithm image enhancement techniques

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The Management System of IoT Informatization Training Room Based on Improved yolov4 Detection and Recognition algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第2期15卷 980-989页

作者： Hu, Huiling Nanjing Vocat Univ Ind Technol Sch Econ Management Nanjing 210023 Peoples R China

In response to the problems of low recognition rate and long system operation time in equipment detection management in the existing IoT information training room management system. A research has proposed an IoT information training room equipment detection management system on the ground of an improved yolov4 detection and recognition algorithm to solve the above problems. Firstly, it used the yolov algorithm to detect and identify equipment in the IoT information training room. Then, it used clustering methods to improve the yolov algorithm, thereby enhancing the detection accuracy and robustness of the algorithm, and thereby enhancing the performance of the equipment management system in the equipment management process of the training room. Finally, performance validation of the training room management system was conducted using datasets and simulation experiments. The results showed that the loss value of the training room equipment management system constructed using the improved yolov4 algorithm during the training process was 0.16. The accuracy and recall rates of device recognition were 95.71% and 92.83%, respectively. And the detection false alarm rate during the device detection and recognition process was only 2.15%, with a mAP value of 91.66%, and the detection and recognition indicators are higher than those of the comparison method. This indicates that the training room equipment management system constructed in the study has good adaptability in equipment detection and recognition in IoT information training rooms. The research aims to provide effective technical support for the management system of IoT training room equipment.

关键词： yolov4 algorithm Internet of Things informatization training room management system detection and recognition

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yolov4-based hybrid feature enhancement network with robust object detection under adverse weather conditions

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第5期18卷 4243-4258页

作者： Patil, Shankar M. Pawar, Shivaji D. Mhatre, Sonali N. Kharade, Prakash A. Smt Indira Gandhi Coll Engn Navi Mumbai India AIKTC Tech Campus New Panvel India Bharati Vidyapeeth Coll Engn Navi Mumbai India

Investigations into the behaviour of pedestrians and autonomous driving both frequently employ object detection. It has always been a popular area for research in computer vision and artificial intelligence. Because of the advancement of deep learning, object detectors are improving in accuracy and speed. However, the majority of them struggle to reconcile speed and precision. Because of this, the object detection model employed in this work, which is based on a better You Only Look Once Version 4 (yolov4) algorithm, considers both detection accuracy and efficiency. Several different sensor types, including cameras and mmWave (millimetre Wave) radar, are employed to observe the environment. This study employs a yolov4-based hybrid feature enhancement network for robust object detection under adverse weather conditions (AWC), utilizing the CRUW dataset. A camera-radar-fused approach that detects things that are cross-supervised by a deep radar object detection network, such as the yolov4 algorithm, reduces processing complexity. To correct category imbalance and further boost the robustness of the suggested model, the class-balanced sampling strategy (CBSS) is employed. Python software was employed to analyse the suggested method. In terms of mAP and accuracy, the suggested method is contrasted with the current OD ensemble framework and faster R-CNN methodologies. The suggested method's accuracy and mAP are higher than those of the other existing methods;as a result, the proposed method's mAP produces 40% and its accuracy produces 98.7%, respectively. Additionally, the suggested method performs better when classifying the object and detecting it, but it also strengthens the system's stability and reduces category imbalance.

关键词： yolov4 algorithm Object detection mmWave radar Camera-radar-fused algorithm Class-balanced sampling strategy Multipoint voxel convolution Multi-feature fusion network

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Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

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International Journal of Digital Earth 2023年第1期16卷 910-936页

作者： Fang Huang Shengyi Chen Qi Wang Yingjie Chen Dandan Zhang School of Recourses and Environment University of Electronic Science and Technology of China(UESTC)ChengduPeople’s Republic of China Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS)BeijingPeople’s Republic of China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China

For a majority of remote sensing applications of unmanned aerial vehicles(UAVs),the data need to be downloaded to ground devices for processing,but this procedure cannot satisfy the demands of real-time target *** objective in this study is to develop a real-time system based on an embedded technology for image acquisition,target detection,the transmission and display of the results,and user interaction while providing support for the interactions between multiple UAVs and *** work is divided into three parts:(1)We design the technical procedure and the framework for the implementation of a real-time target detection system according to application requirements.(2)We develop an efficient and reliable data transmission module to realize real-time cross-platform communication between airborne embedded devices and ground-side servers.(3)We optimize the yolov4 algorithm by using the K-Means algorithm and TensorRT inference to improve the accuracy and speed of the NVIDIA Jetson *** experiments involving static detection,it had an overall confidence of 89.6%and a rate of missed detection of 3.8%;in experiments involving dynamic detection,it had an overall confidence and a rate of missed detection of 88.2%and 4.6%,respectively.

关键词： Unmanned aerial vehicle(UAV) embedded system deep learning yolov4 algorithm data transmission vehicle detection

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An infrared image detection of power equipment based on super-resolution reconstruction and yolov4

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JOURNAL OF ENGINEERING-JOE 2022年第10期2022卷 1006-1016页

作者： Wu, Junpeng Li, Xianglei Zhou, Yibo Northeast Elect Power Univ Minist Educ Key Lab Modern Power Syst Simulat & Control & Ren Jilin 132012 Jilin Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin Jilin Peoples R China

Infrared imaging technology is widely used in the fault diagnosis of power equipment. The power equipment images obtained by infrared thermal cameras generally have the characteristics of low resolution, low contrast, similar geometric features, and complex image backgrounds. The traditional infrared image detection methods have problems of low detection accuracy, low real-time performance, and missed and fault detection, all of which will lead to great difficulties in the automatic identification process of infrared images of power equipment. This paper adopted an algorithm based on You Only Look Once (YOLO) v4 to realize the detection of electrical equipment under infrared images. First of all, the authors utilized the Fast Super-Resolution Convolutional Neural Network (FSRCNN) algorithm to perform super-resolution reconstruction on the infrared images of power equipment to achieve image enhancement. Moreover, the lightweight network Mobilenetv3 was selected as the backbone network of yolov4 to improve the running speed by reducing network parameters. Finally, the performance of the yolov4 algorithm was compared to verify the effectiveness of the algorithm proposed in this paper. The experimental results showed that the proposed method had strong generalization ability and reliable detection of infrared targets of various types of power equipment, with the mean average precision (mAP) reaching 91.3%.

关键词： complex image backgrounds fault detection low detection accuracy infrared imaging power apparatus infrared images image reconstruction convolutional neural nets infrared targets feature extraction traditional infrared image detection methods image enhancement fault diagnosis yolov4 algorithm infrared thermal cameras cameras Fast Super-Resolution Convolutional Neural Network imaging technology electrical equipment object detection super-resolution reconstruction image resolution power equipment images

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