Application of SAM-based prompting algorithm in camouflage object detection
Jiatang Yuan · 2024
We propose a prompting algorithm based on SAM and apply it to the field of camouflage object detection. We extract the camouflaged object detection (COD) network features after weakly supervised learning and generate bounding box information and point prompt information to guide the SAM to segment the camouflaged objects in the image. The proposed method can effectively improve the performance of camouflage object detection network and has achieved good results on the mainstream camouflage object detection dataset.