Underwater Object Detection Based on Improved Single Shot MultiBox Detector

Abstract

Underwater optical images are scarce, and there are varying degrees of blur and color distortion, which brings great challenges to the detection of underwater objects. In view of the shortcomings of the original Single Shot MultiBox Detector (SSD), in this paper, a shallow object detection layer is added to the original SSD model to improve the network's ability to detect small objects. At the same time, this article improves the confidence loss to narrow the ability of SSD to detect different types of objects. Using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to process the original images, enhance the feature information of the objects in the underwater images. Training the improved SSD network through transfer learning to overcome the limitations of insufficient underwater images. Experimental results show that the algorithm proposed in this paper has better detection performance than the original SSD, YOLO v3 and other algorithms, which is of great significance to the realization of underwater object detection.