A Small Object Detection Method for Drone-Captured Images Based on Improved YOLOv7

Abstract

Due to the broad usage and widespread popularity of drones, the demand for a more accurate object detection algorithm for images captured by drone platforms has become increasingly urgent. This article addresses this issue by first analyzing the unique characteristics of datasets related to drones. We then select the widely used YOLOv7 algorithm as the foundation and conduct a comprehensive analysis of its limitations, proposing a targeted solution. In order to enhance the network’s ability to extract features from small objects, we introduce non-strided convolution modules and integrate modules that utilize attention mechanism principles into the baseline network. Additionally, we improve the semantic information expression for small targets by optimizing the feature fusion process in the network. During training, we adopt the latest Lion optimizer and MPDIoU loss to further boost the overall performance of the network. The improved network achieves impressive results, with mAP50 scores of 56.8% and 94.6% on the VisDrone2019 and NWPU VHR-10 datasets, respectively, particularly in detecting small objects.

Research on Bidirectional Multi-Span Feature Pyramid and Key Feature Capture Object Detection Network

“…Yu et al [26] focused on the issues of occlusion and overlap of objects in RS images, emphasizing the strategy of large-scale proposal bounding boxes, and constructed a novel spatial adaptive detector (RSADet). Zhao et al [27] introduced non-striding convolution and an attention mechanism into YOLOv7 to improve the feature extraction capability for small targets, and optimized the fusion process of deep information for small targets using the Lion optimizer. In article [28], a joint motion mechanism based on a three-degree-of-freedom (DOF) framework was designed for drones in complex motion patterns to achieve real-time active tracking of targets.…”