RF-DET: Refocusing on the small-scale objects using aggregated context for accurate power transmitting components detection on UAV oblique imagery

Abstract

In transmission lines, regular inspections are crucial for maintaining their safe operation. Automatic and accurate detection of power transmission facility components (power components) in inspection imagery is an effective way to monitor the status of electrical assets within the Right of Ways (RoWs). However, the multitude of small-scale objects (e.g. grading rings, vibration dampers) in inspection imagery poses enormous challenges. To address these challenges, we propose a coarse-to-fine object detector named RF-DET. It adopts a Refocus Framework to refine the detection accuracy of small objects within the Regions of Interest of the Power Components (P-RoIs) generated through explicit context. On the basis above, an Implicit Context Aggregation Attention Module (ICAM) is proposed. ICAM utilizes a multi-branch structure to capture and aggregate multi-directional positional and global information, enabling in-depth mining of the implicit context among small objects. To verify the performance of this detector, a benchmark dataset named DOPI-UAV is constructed, comprising 4,438 UAV oblique images and 54,591 instances, encompassing six common categories of power components and one category of defect. Experimental results show that RF-DET achieves mAP of 62.7%, 55.7%, 84.6%, and 52.8% on the DOPI-UAV, Tower, CPLID, and InsD datasets, respectively. Compared to the state-of-the-art method, such as YOLOv9, RF-DET attains significant performance improvements, with increases of 5.2% in mAP and 6.4% in mAP₅₀, respectively. Especially, the AP_S shows an improvement of 8.3%. The datasets and codes are available at https://github.com/DCSI2022/RF-DET.