Resumen
In the field of ship detection, most research on lightweight models comes at the expense of accuracy. This study aims to address this challenge through a deep learning approach and proposes a model DWSC-YOLO, which is inspired by YOLOv5 and MobileNetV3. The model employs a lightweight framework as the backbone network, and the activation function and attention mechanism are researched. Furthermore, to improve the accuracy of the convolutional neural network and reduce loss, heterogeneous convolutions are added to the network. Three independent experiments were carried out using the proposed model. The experiment results show that the model can achieve excellent detection results with a small number of computational resources and costs. The mAP" role="presentation">??????mAP
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of the model is 99.5%, the same as YOLOv5, but the volume is 2.37 M, which is 79.8% less.