Proceedings Article | 24 November 2021
KEYWORDS: Target recognition, Visualization, Visual process modeling, Convolution, Convolutional neural networks, Target detection, Feature extraction, Data modeling, Detection and tracking algorithms, Statistical modeling
The surface ship target recognition technology based on visual perception is an important research direction in the development of maritime unmanned systems, for the reason that it is the main technical means to ensure the reliable completion of tasks of maritime unmanned systems such as the shipborne unmanned aerial vehicle or the unmanned surface vehicle. In recent years, deep learning technology, especially the deep convolutional neural network, performs well in image classification, target recognition and other tasks, introducing it into the ship target recognition field will promote the breakthrough of ship target recognition technology. Many researchers have introduced the deep convolutional neural networks into the field of ship target recognition and achieved good recognition results. However, due to the fixed position sampling mode of convolution operation and the limitation of the receptive field range in the convolutional neural network, the convolutional neural network generally only extracts the feature information related to the target itself, ignoring the interaction information between different targets and between the target and the scene, thus it has poor adaptability to objects’ spatial geometric transformation, which will affect the recognition performance of ship targets with different scales and different heading directions under occlusion. The human visual perception system can recognize the target quickly and accurately when faced with target scale changes, brightness changes, shape changes, and target occlusion, which largely depends on its inherent visual attention mechanism. Aiming at the problem that the performance of the ship target recognition method based on the convolutional neural network is greatly reduced in the occlusion situation, a convolutional neural network model based on the biological visual attention mechanism was constructed, which can recognize the ship targets with different scales and different heading directions under occlusion quickly and effectively. The model used the residual module with dilated convolution to expand the receptive field of the high-level convolution kernels in the basic feature extraction module and integrate more contextual information into the high-level features. The visual attention module quickly extracted features which were highly related to the target and the current task, thus improving the efficiency and enhancing the model’s adaptability to the geometric transformation of the target space. The multi-scale feature fusion module enhanced the features’ comprehensive expression ability, improved the model’s adaptability to the target scale transformation, and reduced the calculation amount of target location and category prediction. The non-maximum suppression algorithm used the re-scoring mechanism to improve the accuracy of target location and category prediction. The ship target recognition results in the ship target test set with different scales and different heading directions under occlusion which obtained by the proposed method and those of the four mainstream methods based on convolutional neural network were compared, the comparison results show that the average ship target recognition accuracy of the ship target recognition method based on biological visual attention mechanism is improved by 17.51% when comparing with the method which has the highest average recognition accuracy within the four mainstream target recognition methods, its average ship target recognition accuracy reaches to 87.69% which shows strong robustness, the recognition rate meets the real-time requirements meanwhile. The above results show that the proposed method effectively solves the problems of poor adaptability to spatial geometric transformation and loss of valid information caused by the fixed position sampling mode of convolution operation and the limitation of the receptive field range.
