Human Detection Algorithm Based on Improved YOLO v4

Zhou, Xuan; Yi, Jianping; Xie, Guokun; Jia, Yang; Xu, Genqi; Sun, Min

doi:10.5755/j01.itc.51.3.30540

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…They evaluated multiple YOLObased models and network training configurations with different datasets to improve the detection results of player identification in handball images. In order to address incomplete human detection in specific video frames, Zhou et al [67] proposed an enhanced YOLO v4 algorithm, which incorporates the Ghost module in the CBM module to reduce the number of parameters further.…”

Section: Deep Learning Methods For Human Detectionmentioning

confidence: 99%

Beyond Human Detection: A Benchmark for Detecting Common Human Posture

Li,

Wu,

Chen

et al. 2023

Sensors

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Human detection is the task of locating all instances of human beings present in an image, which has a wide range of applications across various fields, including search and rescue, surveillance, and autonomous driving. The rapid advancement of computer vision and deep learning technologies has brought significant improvements in human detection. However, for more advanced applications like healthcare, human–computer interaction, and scene understanding, it is crucial to obtain information beyond just the localization of humans. These applications require a deeper understanding of human behavior and state to enable effective and safe interactions with humans and the environment. This study presents a comprehensive benchmark, the Common Human Postures (CHP) dataset, aimed at promoting a more informative and more encouraging task beyond mere human detection. The benchmark dataset comprises a diverse collection of images, featuring individuals in different environments, clothing, and occlusions, performing a wide range of postures and activities. The benchmark aims to enhance research in this challenging task by designing novel and precise methods specifically for it. The CHP dataset consists of 5250 human images collected from different scenes, annotated with bounding boxes for seven common human poses. Using this well-annotated dataset, we have developed two baseline detectors, namely CHP-YOLOF and CHP-YOLOX, building upon two identity-preserved human posture detectors: IPH-YOLOF and IPH-YOLOX. We evaluate the performance of these baseline detectors through extensive experiments. The results demonstrate that these baseline detectors effectively detect human postures on the CHP dataset. By releasing the CHP dataset, we aim to facilitate further research on human pose estimation and to attract more researchers to focus on this challenging task.

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Section: Deep Learning Methods For Human Detectionmentioning

confidence: 99%

Beyond Human Detection: A Benchmark for Detecting Common Human Posture

Li,

Wu,

Chen

et al. 2023

Sensors

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“…Recently, deep learning neural networks have played a significant role in object detection [5,6], not only improving detection accuracy but also promoting detection speed. Generally, there exists three mainstream approaches, including one-stage [7][8][9] and two-stage methods [10][11][12][13], and the Transformer-based method [14,15]. The single-stage methods, exemplified by YOLO (You Only Look Once) algorithms [7], directly predict the bounding boxes and class probabilities in a single stage.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

Cai,

Nie,

Tong

et al. 2024

Electronics

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To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters.

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“…[16][17][18][19][20]. The Yolo algorithm has evolved into a series, comprising Yolov1, Yolov2, Yolov3, Yolov4, Yolov5, and Yolox, among others [21][22][23][24][25][26]. Beginning with the introduction of anchor-based detection in Yolov1, Yolox employs anchor-free detection by directly extracting and regressing features from images or videos.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm

Wang,

Dong,

Bai

et al. 2024

Applied Sciences

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In order to rapidly detect pest types in farmland and mitigate their adverse effects on agricultural production, we proposed an improved Yolox-tiny-based target detection method for farmland pests. This method enhances the detection accuracy of farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). In the experiments, images of pests common to seven types of farmland and particularly harmful to crops were processed through the original Yolox-tiny model after preprocessing and partial target expansion for comparative training and testing. The results indicate that the improved Yolox-tiny model increased the average precision by 7.18%, from 63.55% to 70.73%, demonstrating enhanced precision in detecting farmland pest targets compared to the original model.

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Human Detection Algorithm Based on Improved YOLO v4

Cited by 15 publications

References 20 publications

Beyond Human Detection: A Benchmark for Detecting Common Human Posture

Beyond Human Detection: A Benchmark for Detecting Common Human Posture

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm

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