ASSD: Feature Aligned Single-Shot Detection for Multiscale Objects in Aerial Imagery

Abstract

Object detection is a fundamental part of the interpretation of remote sensing imagery. The one-stage object detector has been adopted into this field because of its high computational efficiency. However, this detector suffers from the misalignment among predefined anchor, object, and feature extracted by standard convolution kernel both in spatial and scale. It limits the further improvement of performance, especially for the long-narrow and multiscale geospatial objects. In this article, the problem is defined as <i>the feature misalignment</i> problem. To deal with this issue, an efficient feature aligned single-shot detector (ASSD) is proposed, which consists of two modules: a novel pseudo anchor proposal module (PAPM) and a flexible context-based feature alignment module (CFAM). The PAPM replaces the regular anchor group with the proposed core anchor and refines it to get aligned locations. It can tackle the spatial misalignment between anchors and their corresponding objects and alleviate the negative/positive imbalance problem. Then, the CFAM adaptively adjusts the sampling points of the convolution kernel and collects the context information according to the aligned core anchor. This plug-and-play module can effectively rectify the misalignment between kernel and objects and extract aligned and robust features. A series of comprehensive experiments are conducted on two large-scale public remote sensing object detection datasets. Experiment results suggest that the proposed method is effective to alleviate the misalignment problem. Compared with the baseline model, the detection accuracy is improved by 8.5&#x0025; mAP and 11.0&#x0025; mAP on the challenging benchmark for object detection in optical remote sensing image (DIOR) and a large-scale dataset for object detection in aerial image (DOTA) dataset, respectively. Our best-resulting model achieves the state-of-the-art performance, surpassing other one-stage detectors both on the two datasets at a high detection speed of 21 FPS.