Research on weakly supervised target localization in sonar imagery based on GradCAM

In the unique marine environment, sonar, as a crucial tool for navigation and ranging using sound waves, plays a key role in the recognition and localization of targets in sonar imagery, which is critical to the performance of underwater equipment. In practical applications, fully supervised object detection methods are commonly used to accurately determine the categories and locations of targets in sonar images. However, these methods require laborious individual annotation of target positions and categories. Addressing this challenge, this paper proposes a weakly supervised target localization method for sonar imagery based on Grad-CAM technology. Initially, the method employs the Balanced Ensemble Transfer Learning (BETL) algorithm for target classification in sonar images, utilizing deep transfer learning. Subsequently, the GradCAM technique is applied to the task of sonar image target classification, using the generated heat maps for target localization. This aims to enhance the accuracy and efficiency of sonar image recognition and localization while reducing the burden of annotation work.