Road Vehicle Detection Based on Deep Learning

To address the challenges of low accuracy in vehicle detection for small targets, occluded targets, and scenes with limited lighting conditions, this paper proposes a vehicle detection method based on YOLOv7. Firstly, a pyramid pooling structure incorporating depth-wise separable and dilated convolu...

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Bibliographic Details
Published in2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT) pp. 1456 - 1460
Main Authors Cui, Jingna, Xing, Dongqiu, Gao, Xinwei, Qi, Lihua, Yu, Ruifeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.04.2024
Subjects
Attention Mechanism
Computational modeling
Convolution
Deep learning
Depth-wise Separable Convolution
Dilated Convolution
Lighting
Road vehicles
Target recognition
Vehicle detection
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DOI10.1109/ICCECT60629.2024.10545867

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Summary:To address the challenges of low accuracy in vehicle detection for small targets, occluded targets, and scenes with limited lighting conditions, this paper proposes a vehicle detection method based on YOLOv7. Firstly, a pyramid pooling structure incorporating depth-wise separable and dilated convolution modules is constructed to allow the model to integrate contextual information while enlarging the receptive field and reducing the parameter count. Next, parallel channel-spatial attention modules are designed to eliminate interference caused by serial connections. Experimental results demonstrate that the proposed detection model achieves an average precision (mAP) of 98.9% and 83.4% for mAP50 and mAP50:95, respectively. Compared to YOLOv7, this represents an improvement of 2 and 5.8 percentage points, while achieving a processing speed of 39 frames per second.
DOI:10.1109/ICCECT60629.2024.10545867