Weakly supervised ship detection in remote sensing images

Ship detection in remote sensing images is important for maritime surveillance. With the rapid development of earth observation technology, high-resolution imaging satellites can provide more observational information. In the face of massive remote sensing data, object-level annotation requires a lot of time and manpower. Weakly supervised object detection is trained using only image-level annotations, thus reducing the requirement for object-level annotations. However, there are still some problems in the detection of weakly supervised ships in remote sensing images, because of the complex, dense distribution and diverse scale characteristics of the ship environment. We propose a weakly supervised object detection method that combines Transformer with weakly supervised learning for ship detection in remote sensing images. First, Proposal Clustering Learning (PCL) for weakly supervised object detection is used as the baseline to detect ships, and the network is continuously refined for better detection performance. Second, the prior location and size information is added to the features of the proposal through the transformer module. These additional information can be used as an important basis for judging whether the proposal is optimal, thereby improving the detection performance. To evaluate the effectiveness of our method, extensive experiments are conducted on a complex dataset of large-scene remote sensing ships. Experimental results show that our method achieves better detection performance than other methods.