Dual-channel feature extraction network for camouflaged small object detection

Small object detection is a challenging problem in object detection, in practice, we found a special kind of small object detection results are very poor, we call it camouflaged small object, the color of the object itself is similar to the surrounding environment, that is, the contrast between the object and the surrounding environment is low. Therefore, we propose a method to stretch the grayscale map to directly increase the contrast between the object and the surrounding environment. Then, as to how to effectively fuse the feature of object in grayscale map with the feature of object in original RGB image, we propose a dual-channel feature fusion module DCF, which allows the features extracted from the grayscale map to enhance the features extracted from the original RGB image. We also constructed a camouflaged small object detection dataset SSD, which is a smoking object detection dataset with only one smoking object, containing 4349 images and 4705 object instances. Our proposed DCF framework can be easily embedded into existing object detection frameworks. We experimented on a number of different excellent detectors, Faster R-CNN, RetinaNet, and YOLOv8, and the results show that our method is effective in all of these detectors.