A Weakly Supervised Slender Object Detection Network for Fishing Rods in UAV Inspection Images

Abstract

The timely detection of fishing activities supports the protection of water resources and aids law enforcement in fishing ban and shoreline inspection scenarios. Fishing rods are highly discriminative objects for identifying such activities; however, their slender shapes and varying orientations make feature extraction and precise localization difficult to achieve. The existing slender object detectors typically require oriented bounding-box supervision, resulting in high annotation costs and unstable angle regression. To address these issues, we propose a weakly supervised slender object detection network (WSSO-DETR) with a low annotation cost. It employs horizontal-box supervision and exploits angle relation consistency and scale invariance across multiview transformations, reducing the need for oriented box annotations. The scale and orientation estimated by the horizontal box branch are used to construct a long-axis prior, based on which an oriented object reference point generation module (OORPG) is designed. We further introduce a point set-based oriented decoder (PSOD) and an encoder–decoder feature fusion-based prediction head (E-DFFPH) to model the interactions among object-related point sets and attain improved localization performance. Experiments show that WSSO-DETR improves the mAP50 metric by 2.5–15.3% on Slender COCO, by 1.1–4.8% on Fishing Detect, and by 1.7–4.9% on DOTA-v1.0, demonstrating its effectiveness and generalizability.