MFFSSD: An Enhanced SSD for Underwater Object Detection

Abstract

The performance of deep neural networks in object detection is impressive, however, direct use of existingalgorithms may not obtain satisfactory resultsdue to the characteristics of underwater images. In this paper, a multi-scale feature fusion Single Shot Multibox Detector (MFFSSD) based on the SSD algorithm is proposed to improve the accuracy of underwater object detection. There are two main improvements: introducing attention module and perform multiscale feature fusion. By introducing attention module, the features of the input image obtained through convolution are weighted in both channel and space aspect, so as to highlight the useful informationto overcomes the problem of small and blur objects which are common in underwater images. By extracting a lower level feature map forward, more details of the lower level image can be obtained to adapt to the underwater object with simple shape, so that low-level features have a higher contribution to the recognition object; at the same time, the high-level feature map is enlarged by using deconvolution, and then fused with the low-level feature map to introduce more contextual information. The fusion of the features extracted from the shallow and high level networks ensures the efficiency of the features to the greatest extent. Experiments on URPC show that MFFSSD significantly improved the underwater object recognitionaccuracy compared to the original algorithm.