# Underwater Object Detection Method Based on Improved Faster RCNN
## 1 Introduction  
Marine biological resources on the earth are abundant and diverse [1], and it is especially important to effectively detect the growth of marine organisms and manage them scientifically in order to better understand, utilize and protect these limited marine resources. However, little is known about the seafloor, and darkness, high pressure, and extreme temperatures of the seafloor can cause many adverse effects on exploration efforts. For example, light decays quickly underwater and there is insufficient light, resulting in blurred and unclear images of marine organisms, and various noise and image distortion problems. In particular, there are various limitations of traditional manual means to explore underwater environments: they are time consuming, high cost, and have limited detection range and defects in acquired images. Therefore, how to conduct stable, reliable, and fast underwater object detection and monitoring in complex and harsh underwater environments is an urgent problem to be solved.  
In view of complex underwater environments and the prevalence of image noises, this paper chose the Faster RCNN as the basis. Firstly, the initial image dataset is preprocessed and extended with data enhancement. Secondly, the feature extraction module of the FasterRCNN is replaced by Res2 Net101 [2]. The OHEM algorithm [3] is introduced to solve the imbalance of positive and negative samples of the bounding box. Thirdly, the regression mechanism of the bounding box is optimized. Finally, the training strategy is optimized so that the improved Faster RCNN model can detect the underwater objects accurately and effectively.  
The main novel points of this paper are summarized as follows:  
1. A two-stage underwater object detection method is proposed and improved, which is based on the Faster RCNN, and its initial image dataset is subjected to Mosaic [4] data enhancement and related preprocessing. The improved Faster RCNN model has stable performance and good accuracy, and is more conducive to underwater object detection.
2. VGG16 is replaced by Res2 Net101 in the feature extraction module of the improved Faster RCNN, because the structure of Res2 Net101 can enhance the expression ability of the receptive field in the network layer of the improved Faster RCNN. The OHEM algorithm is introduced to effectively solve the imbalance problem of the positive and negative samples of the candidate prior frame.
3. The IOU structure in the improved Faster RCNN is replaced by GIOU [5], and the non-overlapping area between the candidate prior frame and the real object is also taken into account, so that the weakness of the original IOU can be weakened and the improved Faster RCNN can better optimize the candidate prior frame in the training process.
4. The original NMS algorithm is replaced by the Soft-NMS algorithm [6], which only needs simple modification and no additional parameters, is easy to realize and can be easily applied to different object detection algorithms. The training strategy of the improved model is optimized by a multi-scale training approach [7], which can improve the robustness of the improved Faster RCNN detection algorithm to different object sizes.