A dilated convolution-based feature adaptation method for detection of high aspect ratio objects in aerial images

Abstract

In real scenarios, objects with high aspect ratios are actually very common, and such objects hold significant importance in the field of object detection. However, most of the existing object detection algorithms tend to overlook this specific type of object. After analyzing the statistical data, we observed a substantial decrease in mAP (mean Average Precision) for classical object detection algorithms when they are tasked with detecting only high aspect ratio objects. Therefore, we conducted an analysis of the factors that influence the detection performance of these objects and made the following improvements: (1) We introduced large-kernel attention convolution between the backbone network layers. This addition allows each position feature to have a larger receptive field, facilitating better feature learning; (2) By incorporating multiple sets of deformable convolutions for feature-adaptive processing, we were able to enhance the learning of characteristic information specific to the object itself. This approach also promotes network convergence. The proposed method yielded a significant improvement in accuracy, approximately 5[Formula: see text] higher than the baseline, when evaluated on the FGSD2021 dataset. Furthermore, our method outperformed the current best method by approximately 0.5[Formula: see text].