MCD-YOLO: An Improved YOLOv11 Framework for Manhole Cover Detection in UAV Imagery

Abstract

Unmanned aerial vehicle (UAV) remote sensing provides an effective solution for monitoring urban infrastructure. Accurate detection of manhole covers in high-resolution UAV imagery is essential for the safe operation and maintenance of underground utility networks. However, detecting manhole covers in complex urban environments remains challenging due to their small size, visual similarity to surrounding structures, and frequent occlusion. To address these challenges, we propose a novel detection model termed manhole cover detection YOLO (MCD-YOLO). First, to exploit the regular geometric structure of manhole covers, we design an EdgeExtract module to enhance the C3k2 block in the backbone network. This module fuses image gradient information and high-frequency features to strengthen the geometric edge representation of manhole covers, thereby improving their discriminability against complex backgrounds. Second, we propose an oriented context interaction (OCI) module that employs multiorientation depthwise separable convolutions to capture both local features and global contextual dependencies, effectively suppressing interference from structurally similar background elements. Finally, we design a distribution-guided localization (DGL) module that dynamically calibrates classification confidence based on the statistical distribution of bounding box regression offsets, significantly reducing high-confidence false positives caused by localization errors under occlusion. Extensive experiments on our self-constructed manhole cover (MHC) dataset and the public VisDrone2019 dataset demonstrate the superior performance of MCD-YOLO.