ABSOD: Attention based small object detector for outfalls inspection in aerial images

Strengthening the inspection of outfalls into rivers and oceans can help monitor pollutant emissions to the natural environment. Unmanned aerial vehicle (UAV) with high spatial resolution imagery has become a more efficient method for outfall surveys. At present, outfalls retrieval from UAV images relies on visual interpretation by skilled experts. However, long periods of concentration on detecting outfalls in high-resolution images for an expert easily increase mental load and stress, resulting in missing and false detection. Therefore, we develop a deep learning model, called Attention Based Small Object Detector (ABSOD), to perform outfalls detection in aerial images. In this model, an adaptive spatial correlation pyramid attention (ASCPA) network is proposed to establish long-distance region-to-region relationships between the outfall and its surrounding information more effectively. This network is mainly composed of SPE (Spatial Pyramid Extractor) and SCFM (Spatial Correlation Fusion Module). The purpose of the SPE is to extract multi-scale spatial information on the feature map. The SCFM is used to perform spatial correlation feature recalibration to selectively emphasized informative features. Experimental results show that the proposed network outperforms the state-of-the-art small object detection model in detecting outfalls, and reaches 45.9%, 92.8%, 86.5% and 34.4% in the four metrics of Precision, Recall, AP 0.5 , and AP 0.5:0.95 , respectively. To show the superiority of the ASCPA network, we compared our results with other attention mechanisms, all of them show that the ASCPA network has a competitive performance for outfalls detection. Moreover, based on visualization analysis, the ASCPA network is able to pay more attention on true outfall objects with respect to other attention mechanisms. These promising results demonstrate that the deep learning algorithm can be a feasible solution to assist experts in detecting outfalls with UAV imagery. The model and code are available at https://github.com/ISCLab-Bistu/ASCPA-Attention .