An Effective Method for Underwater Biological Multi-Target Detection Using Mask Region-Based Convolutional Neural Network

Abstract

Underwater creatures play a vital role in maintaining the delicate balance of the ocean ecosystem. In recent years, machine learning methods have been developed to identify underwater biologicals in the complex underwater environment. However, the scarcity and poor quality of underwater biological images present significant challenges to the recognition of underwater biological targets, especially multi-target recognition. To solve these problems, this paper proposed an ensemble method for underwater biological multi-target recognition. First, the CutMix method was improved for underwater biological image augmentation. Second, the white balance, multiscale retinal, and dark channel prior algorithms were combined to enhance the underwater biological image quality, which could largely improve the performance of underwater biological target recognition. Finally, an improved model was proposed for underwater biological multi-target recognition by using a mask region-based convolutional neural network (Mask-RCNN), which was optimized by the soft non-maximum suppression and attention-guided context feature pyramid network algorithms. We achieved 4.97 FPS, the mAP was 0.828, and the proposed methods could adapt well to underwater biological multi-target recognition. The recognition effectiveness of the proposed method was verified on the URPC2018 dataset by comparing it with current state-of-the-art recognition methods including you-only-look-once version 5 (YOLOv5) and the original Mask-RCNN model, where the mAP of the YOLOv5 model was lower. Compared with the original Mask-RCNN model, the mAP of the improved model increased by 3.2% to 82.8% when the FPS was reduced by only 0.38.

Image Recognition of Mine Water Inrush Based on Bilinear Convolutional Neural Network with Few-Shot Learning

“…Because of the MSE loss function used in this study, the fully connected layer affected the final convergence result. (3) This study provides a new idea and method for identifying water inrush in mines. The method is based on deep learning, which can effectively identify water inrush underground and improve mine production intelligence.…”

“…In recent years, the major accidents in coal mines have been water disasters, causing heavy casualties and property losses. As computer technology has developed, deep learning − technology has gradually been applied to mine engineering − and water resource engineering. − Existing methods have mostly used traditional image recognition, which recognizes water inrush in only a particular scene, but the mine environment is harsh and the image scenes are diverse, so traditional image recognition has difficulty accurately recognizing complex scenes. Compared with traditional image recognition, image recognition algorithms based on deep learning have strong generalization ability and robustness, and they can adapt to image recognition in multiple scenes.…”

WBi-YOLOSF: Improved FPN for aquatic real-time target detection under the Artificial rabbits optimization

“…demonstrating that the WBi-YOLOSF target detection model performs better overall than the other comparison models. The algorithms selected for comparison are Faster RCNN, Mask RCNN, SSD, RetinaNet, YOLOv5, YOLOv7 36 , YOLOv8 37 , YOLOv9 38 , MCCNN39 , RTAL40 , and the method proposed by Yue et al41 in 2023 for underwater multi-target detection. Two-stage target detection algorithms, such as Faster R-CNN and Mask R-CNN, can attain a comparatively high detection accuracy.…”