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5 October 2021 Nighttime vehicle detection on highway based on improved faster R-CNN model
Yiqing Guo, Min Zhao
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Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110W (2021) https://doi.org/10.1117/12.2604624
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China
Abstract
Vehicle monitoring using cameras is an important task for highway management. At present, most vehicle detection algorithms are applied in daytime. However, compared with the daytime, the road environment at night is darker and the vehicle body is not obvious, the existing algorithm is difficult to meet the vehicle detection requirements in the night scene. Due to the complexity of night scenes, video images bluring and excessive noise, vehicle detection at night poses a great challenge. Aiming at the above problems, this paper proposes a nighttime vehicle detection framework based on improved Fasterr R-CNN. Firstly, the deep residual network is used to extract features autonomously, and the spatial attention mechanism is integrated to make the network pay more attention to the road region rather than the background region. Secondly, balanced feature module is introduced to make full use of the extracted visual features. Finally, Soft- NMS replaces NMS to reduce the number of missed-detection vehicles. Experimental results show that the AP value of the improved Faster R-CNN model is 4.18% higher than that of baseline Faster R-CNN model.
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Yiqing Guo and Min Zhao "Nighttime vehicle detection on highway based on improved faster R-CNN model", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110W (5 October 2021); https://doi.org/10.1117/12.2604624
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KEYWORDS
Detection and tracking algorithms
Feature extraction
Convolutional neural networks
Environmental sensing
Image processing
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