Underwater Object Classification in SAS Images Based on a Deformable Residual Network and Transfer Learning  
[PERSON]  
1 Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China 12 University of Chinese Academy of Sciences, Beijing 10049, China 23 Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190, China 3  
[PERSON]  
1 Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China 13 Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190, China 31 Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China 13  
[PERSON]  
1 Institute of Ac  
achieve certain results, they require high professional domain knowledge. Some key information may be lost during the feature extraction, and the robustness and generalization ability of the model is lacking. In recent years, convolutional neural networks, as an important tool for image processing, have independently learned effective features from training data for different tasks and show great superiority in image feature extraction, object detection, and classification efficiency. Features of objects are extracted by the convolutional neural network, and the classification is realized by a support vector machine in [12]. In ref. [13], an automatic target recognition method is proposed, which combines a CNN-based detector with a probabilistic grid map to achieve the detection and recognition of objects in forward-looking sonar images. In ref. [14], the use of a feature pyramid network combined with multi-scale features to automatically detect underwater targets has high efficiency. In the field of object detection, researchers use YOLOV4 and YOLOV3 as the backbone networks to achieve object detection through the use of dense layers and spatial pyramid pooling, offering better performance compared to current popular methods [15; 16; 17]. However, the above convolutional neural networks use fixed-shape convolutional kernels, which are often sampled in regions of no interest to the image and have inherent drawbacks for modeling complex shapes. The deformable convolution proposed by [18] can solve this problem by adding the offset of sampling points on the regular convolution, but the calculation produces a large number of parameters [19; 20; 21]. When using deformable convolutional theory for object classification, it is important to exploit its advantages in feature extraction on the one hand, and to be aware of the problem of long training times due to the complex computational process on the other. Therefore, it is important to choose a suitable backbone network and optimize the network structure. In addition, the problem of small samples is also an important factor limiting the performance of object classification, for which many scholars use the theory of transfer learning for model optimization [22]. To address the above issues, based on existing research, an underwater object classification method for SAS images based on deformable residual network and transfer learning is proposed. The innovation is reflected in several aspects. (1) The method uses ResNet as the basic network and downsampling at the input layer, then extracts the depth features of objects through multiple residual blocks, and the average improvement in classification accuracy is over 6%, compared to other models. (2) The lightweight deformable convolution module DSDCN is designed to increase the sampling offset so that the sampling points of the convolution kernel can change adaptively according to the object shape, to be more adaptable to objects with different shapes. (3) During the calculation of the offset, the depthwise separable convolution is used to reduce the network parameters and computational complexity while enhancing the spatial sampling capability, and the computational complexity can be reduced to one ninth of the original, compared to standard convolution.