Underwater image enhancement and detection based on convolutional DCP and YOLOv5

Underwater image restoration is conducive to better underwater resource detection and information effective transmission. However, the light in the complex water body diffusely reflective and the selection absorption of different band light results in blurring and color distortion of underwater image. Therefore, we propose a convolutional Dark Channel Prior (DCP) underwater image recovery algorithm to enhance underwater images. It can do the pre-processing work for the subsequent YOLOv5 object recognition. The enhancement algorithm first performs Commission International Eclairage Lab (CIELAB) equalization of underwater images for color distortion correction. Meanwhile, the underwater image formation parameters are estimated by the minimum convolution region DCP. Then, Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed to obtain an enhanced underwater image. Finally, the enhanced underwater image is input to the YOLOv5 model for detection. Experimental results show that the proposed method outperforms state-of-art algorithms in terms of image recovery effect, evaluation quality and detection accuracy.