Self-Rectification Faster R-CNN: Enhancing Object Detection in Complex-Background Aerial Images

Detecting objects in aerial images with complex backgrounds remains a significant challenge. This difficulty arises not only from the requirement of precise regression of bounding boxes but also from the presence of confusing backgrounds that share visual similarities with target objects. Existing object detectors primarily focus on improving convolutional representations for objects, with limited attention given to background proposal sampling. This paper introduces the Self-Rectification Faster R-CNN, a novel object detector designed to address the hard background proposal sampling problem for most of the object detector. The proposed method incorporates four key innovations: a balanced batch sampler, a balanced minibatch sampler, a hard background image sampler, and the SRNMS (Self-Rectification Non-Maximum Suppression) technique. Experiments demonstrate that the Self-Rectification Faster R-CNN achieves at least 7% improvement in F1-score compared to three state-of-the-art object detectors evaluated on our waterfowl detection dataset.