A Method for Small-area Oil Spill Detection by Introducing Attention Mechanism into DeeplabV3+ Network
The dangers of oil spills at sea are extensive and widespread,and the time and spatial uncertainties are significant.Therefore,high-precision identification and monitoring of marine oil spills are of great importance to the protection of the global ecological environment.This paper addresses the problem of inaccurate classification of small-scale oil spills by traditional convolutional neural network models,proposing a high-precision extraction method for small-scale oil spills from SAR images based on deep learning.This method is based on the DeeplabV3+network model and improves the network's accuracy in classifying small-scale oil spills on the sea surface by introducing the SE attention mechanism.The paper establishes a sea surface oil spill extraction model based on the European Space Agency's open-source"Oil Spill Detection Dataset".The model achieves an MIoU of 77.70％and an MPA of 98.16％in training.By visually comparing the model prediction images and the accuracy metrics of the two classes,it is found that the DeeplabV3+network model,optimized with the attention mechanism,significantly outperforms the original network model.The addition of the attention mechanism has significantly improved the network's effectiveness in monitoring small-scale oil spills.
oil spill in the oceandeep learningattention mechanismDeeplabV3+SAR image
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