Underwater target detection based on Faster R-CNN and adversarial occlusion network

Abstract

Underwater target detection is an important part of ocean exploration, which has important applications in military and civil fields. Since the underwater environment is complex and changeable and the sample images that can be obtained are limited, this paper proposes a method to add the adversarial occlusion network (AON) to the standard Faster R-CNN detection algorithm which called Faster R-CNN-AON network. The AON network has a competitive relationship with the Faster R-CNN detection network, which learns how to block a given target and make it difficult for the detecting network to classify the blocked target correctly. Faster R-CNN detection network and the AON network compete and learn together, and ultimately enable the detection network to obtain better robustness for underwater seafood. The joint training of Faster R-CNN and the adversarial network can effectively prevent the detection network from overfitting the generated fixed features. The experimental results in this paper show that compared with the standard Faster R-CNN network, the increase of mAP on VOC07 data set is 2.6%, and the increase of mAP on the underwater data set is 4.2%.