Abstract
The images captured by vehicle-mounted cameras in low-illumination environments have the problem of severe loss of detailed information. At the same time, the detection and recognition performance of traffic object detection algorithms is also influenced by factors such as object texture, movement speed, shooting angle, and occlusion. Under low-illumination conditions, the background of images is integrated with traffic objects, so the current object detection algorithms have relatively poor performance in detecting traffic objects under low illumination. In order to achieve low-illumination image enhancement without significantly reducing the reasoning speed of object detection algorithms and meanwhile improve the detection accuracy of object detection algorithms under low-illumination conditions, a multi-object detection model based on image enhancement, namely low-illumination enhancement and deep fusion-you only look once (LEDF-YOLO), is proposed. Firstly, based on the generative adversarial network (GAN) model, the direct-to-deep-generative adversarial network (DD-GAN) model is proposed to improve the effect of enhancing low-illumination images. Then, the fusion and parallel-cross stage partial bottleneck with two convolutions (FP-C2f) module and the transformer-spatial pyramid pooling fast (T-SPPF) module were designed to enhance and fuse multi-scale features. Finally, the network model of you only look once version 8n (YOLOv8n) was improved by introducing cross-hierarchical connections, making object localization more accurate. Experimental results on UA-DETRAC and self-made datasets showed that compared to the YOLOv8n algorithm, the LEDF-YOLO object detection method improved detection accuracy while maintaining the high real-time performance of the you only look once version 8n (YOLOv8n) algorithm.
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