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detection in Optical Remote Sensing Images of Transmission Tower Based on oriented object detection

CSEE Journal of Power and Energy Systems 2025年第1期11卷 217-226页

作者： Yuanpeng Tan Fei Jiao Wenhao Mo Haiying Liu Xiaojing Bai Jiaxiu Ma China Electric Power Research Institute Beijing 100192China China Electric Power Research Institute and North China Electric Power University Beijing100192China China Electric Power Research Institute and State Grid Corporation of China Information and Communication Branch Beijing100192China

Transmission towers play a crucial role in overhead transmission line systems and are the key target of transmission line *** the help of remote sensing technology,transmission towers can be effectively detected in wide areas at reasonable costs and in a relatively short time ***,it is difficult to identify the type of transmission towers in optical remote sensing images due to detail degradation caused by long-distance and high-altitude *** paper proposes a transmission tower detection method in optical remote sensing images using an oriented object detector and object and shadow joint *** enrich the information,the transmission towers and their shadows are jointly detected through a CenterNet detector with an orientation prediction *** improve the detection accuracy of difficult objects,attention and deformable convolutional network modules are introduced to the backbone and orientation prediction branches,*** the orientation and the aspect ratio of the objects and shadows,a focal loss function with an aspect ratio is employed to further improve the *** and shadow joint detection are separately realized through the one-box and multi-box detection strategies.A transmission tower dataset RSITT labeled with horizontal and oriented boxes is *** conducted on the RSITT dataset have demonstrated that the detection accuracy and recall rate of the proposed joint detection algorithm reached 73.2%and 95.2%.

关键词： oriented object detection remote sensing image transmission projection transmission tower detection

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for oriented object detection

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Computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for oriented object detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： oriented object detection transformer deep learning

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