To enhance the accuracy of fine-grained classification of shark populations and address issues such as image interference,insufficient extraction of local key features,and lack of semantic correlation between channels,a DM-BCNN model for fine-grained classification of shark populations based on an improved Bilinear Convolutional Neural Network(B-CNN)is proposed.First,deformable convolution is introduced to replace the feature extraction part of the original model with a DRAM_ResNet network structure,enhancing the model's ability to detect complex and irregular shapes and local structures.Then,the NAM attention mechanism is employed to strengthen the model's ability to identify and extract key features.Finally,a Mutual Channel Loss function is introduced to enhance the semantic correlation between different channels of shark images,allowing the model to capture information from various aspects of the images more comprehensively.The results show that the improved DM-BCNN model achieved a Top-1 accuracy of 96.12％,representing a 2.51 percentage point improvement over the original model.The study demonstrates that the proposed improved model outperforms the original model in fine-grained image classification,making it more effective for fine-grained classification and identification of shark populations.
关键词
鲨鱼/细粒度图像/注意力机制/可变形卷积/互通道损失
Key words
shark/fine-grained image/attention mechanism/deformable convolution/mutual channel loss
维普
万方数据