Research on target detection method of underwater robot in low illumination environment

Underwater image detection remains a challenge due to problems such as noise, illumination inhomogeneity and low contrast. To solve these problems, this paper proposes a new level set segmentation model integrating saliency region detection (SDLSE). First, an underwater low-illumination saliency detection model is constructed and the target region is roughly segmented with the help of the saliency detection model to obtain pixel-level a prior shape information. Second, the a prior information is used as the shape constraint for finely segmenting the level set to improve the energy function of the level set. Based on the experimental data and fish dataset, the algorithm is statistically analyzed. It is verified that the segmentation effect of SDLSE model is better than other level sets in terms of segmentation accuracy and time efficiency.