Abstract
Low-light images are images taken in poorly illuminated environments. Such images suffer from colour distortion, loss of detail and blurriness, which seriously affects the detection accuracy of object detection tasks. In order to improve the accuracy of object detection in low-light images, we propose a low-light image object detection algorithm based on image enhancement. The algorithm is jointly trained on the input side of the YOLOv5 network in combination with an unsupervised low-light enhancement model. The training phase optimises the overall network with the loss of object detection so that the image enhancement results are more favourable for improving the object detection accuracy. In the feature extraction phase, we design a feature enhancement model based on an attention mechanism. Our algorithm is tested on the publicly available ExDark dataset and achieves a mean average precision (mAP) of 79.15%, which is a 4.25% improvement over the baseline.
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Acknowledgements
This work was supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant 2022196 and Y202051, in part by the National Natural Science Foundation of China under Grant 61821005, in part by the Natural Science Foundation of Liaoning Province under Grant 2021-BS-023.
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Wang, F., Chen, X., Wang, X., Ren, W., Tang, Y. (2023). Research on Object Detection Methods in Low-Light Conditions. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14270. Springer, Singapore. https://doi.org/10.1007/978-981-99-6492-5_48
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DOI: https://doi.org/10.1007/978-981-99-6492-5_48
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