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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. [ABSTRACT FROM AUTHOR]

3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity. However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely on consistent information from both sensors while ignoring their complementary information. In this paper, we present a novel two-stage multi-sensor deep neural network, called the adaptive learning point cloud and image diversity feature fusion network (APIDFF-Net), for 3D object detection. Our approach employs the fine-grained image information to complement the point cloud information by combining low-level image features with high-level point cloud features. Specifically, we design a shallow image feature extraction module to learn fine-grained information from images, instead of relying on deep layer features with coarse-grained information. Furthermore, we design a diversity feature fusion (DFF) module that transforms low-level image features into point-wise image features and explores their complementary features through an attention mechanism, ensuring an effective combination of fine-grained image features and point cloud features. Experiments on the KITTI benchmark show that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]