Abstract
<p indent=0mm>In the case of surface target monitoring, the clarity of the ship target often varies with the reflection intensity of the sea surface under different sunlight intensity, which will lead to the unstable recognition rate of the ship target and increase the false alarm rate. For this reason, the ship target recognition algorithm based on ResNet-50 is proposed. Firstly, it uses ResNet-50 network to extract image feature information and applies sunlight robust loss constraint to the features before and after sunlight intensity change to reduce the feature difference. Then, it uses gray-scale histogram to calculate the statistical matrices of features to obtain six features: light contrast, brightness, smoothness, information, third-order matrices and entropy, and generates new feature vector to apply sunlight robust loss constraint to the features before and after sunlight intensity change again. Finally, the two constraints are combined to form a loss function and trained to optimize the optimal weights using Bayesian adaptive hyperparameters. The experimental results show that the average recognition rate of the database for ship sunlight variation reaches 90.47%, which is about 4.00% higher than that before the improvement, and the recognition rate of ship images with sunlight variation of 25, 65 and 80 increases by 3.14%, 6.07% and 16.41%, which shows that the algorithm has a good constraint effect on sunlight variation and the recognition rate is significantly improved.
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