Low-illumination Object-Detection Algorithm Based on Image Adaptive Enhancement

In the case of detection tasks in low-light environments, owing to the influence of unfavorable factors, such as low brightness, low contrast, and noise, missed and wrong detections can occur. Hence, a low-light object detection algorithm based on image adaptive enhancement is proposed. Combining conventional image processing methods with deep learning, an image adaptive enhancement network is designed, where multiple adjustable filters are combined in cascade to gradually enhance the input low-light image, and the adjustment parameters of each filter are predicted using a convolutional neural network based on the global information of the input image. The adaptive enhancement network is combined with the YOLOv5 object detection network for end-to-end joint training such that the image enhancement effect is more conducive to object detection. As the low-light object detection process is susceptible to missed detection, the channel attention mechanism SE-Net is improved, and a feature enhancement network is designed and embedded into the end of the Neck region of the YOLOv5 network to reduce the loss of information about potential target features caused by the process of fusion of network features. Experimental results show that the proposed algorithm achieves a detection accuracy of 77.3% on the low-light dataset ExDark, which is 2.1 percentage points higher than that afforded by the original YOLOv5 object detection network, and its detection speed reaches 79 frame/s, which affords real-time detection.