ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Oriented detection algorithm for insulator and their defects from aerial images based on improved ReDet
Received date: 2025-01-20
Revised date: 2025-03-13
Accepted date: 2025-05-12
Online published: 2025-05-27
Supported by
National Natural Science Foundation of China(62273091);Science and Technology Project of Provincial Managed industrial Units of State Grid Jiangsu Electric Power(JC2024074)
To address the challenges of insulators in UAV aerial images， such as multi-scale， diverse morphologies， arbitrary orientations， and inconspicuous defect features， an improved object detection algorithm based on ReDet is proposed. First， a Rotation Equivariant Attention Module （ReCBAM） is integrated into the backbone network to enhance the focus on target regions. Second， a novel rotation-equivariant recursive feature pyramid is constructed using dilated convolution group， self-gated activation functions， and an adaptive feature fusion module， providing stronger multi-scale and multi-morphological feature modeling capabilities. Finally， the RoI detector is extended to a four-stage hybrid cascade structure， incorporating contextual information fusion and an improved loss function to refine the feature representation of target regions. The improved loss function combines KL divergence， the Focal Loss mechanism， and an IoU-based dynamic weighting mechanism， establishing a geometric-spatial dynamic joint optimization mechanism for rotated bounding boxes， thereby effectively improving the localization accuracy. Experimental results on a self-built dataset show that the proposed algorithm achieves a mAP of 95.04% under oriented bounding box annotations， representing a 3.01% improvement over the baseline ReDet， and demonstrates superior performance in detecting small， rotated targets under complex backgrounds. Additionally， the model’s parameter size is only 34.57 MB， making it suitable for deployment on UAV platforms. Under horizontal bounding box annotations， the algorithm achieves a mAP of 95.56%， with a 1.57% improvement over the baseline ReDet， validating the algorithm’s strong generalization capability.
Yi ZHENG , Xianghong CHENG , Xingbang TANG , Yi CAO . Oriented detection algorithm for insulator and their defects from aerial images based on improved ReDet[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(18) : 331825 -331825 . DOI: 10.7527/S1000-6893.2025.31825
| [1] | 刘传洋， 吴一全， 刘景景. 无人机航拍图像中绝缘子缺陷检测的深度学习方法研究进展［J］. 电工技术学报， 2025， 40（9）： 2897-2916. |
| LIU C Y， WU Y Q， LIU J J. Research progress of deep learning methods for insulator defect detection in UAV based aerial images［J］. Transactions of China Electrotechnical Society， 2025， 40（9）： 2897-2916 （in Chinese）. | |
| [2] | 罗旭东， 吴一全， 陈金林. 无人机航拍影像目标检测与语义分割的深度学习方法研究进展［J］. 航空学报， 2024， 45（6）： 028822. |
| LUO X D， WU Y Q， CHEN J L. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（6）： 028822 （in Chinese）. | |
| [3] | LIU W， ANGUELOV D， ERHAN D， et al. SSD： Single shot MultiBox detector［M］∥Computer Vision-ECCV 2016. Cham： Springer International Publishing， 2016： 21-37. |
| [4] | LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］∥2017 IEEE International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2017： 2999-3007. |
| [5] | WANG C Y， BOCHKOVSKIY A， LIAO H M. YOLOv7： trainable bag-of-freebies sets new state-of-the-art for real-time object detectors［C］∥2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2023： 7464-7475. |
| [6] | 陈奎， 贾立娇， 刘晓， 等. 基于多尺度特征融合的绝缘子缺陷程度检测［J］. 高电压技术， 2024， 50（5）： 1889-1899. |
| CHEN K， JIA L J， LIU X， et al. Insulator defect degree detection based on multi-scale feature fusion［J］. High Voltage Engineering， 2024， 50（5）： 1889-1899 （in Chinese）. | |
| [7] | 王韵琳， 冯天波， 孙宁， 等. 融合注意力与多尺度特征的电力绝缘子缺陷检测方法［J］. 高电压技术， 2024， 50（5）： 1933-1942. |
| WANG Y L， FENG T B， SUN N， et al. Defect detection method for power insulators based on attention and multi-scale context information［J］. High Voltage Engineering， 2024， 50（5）： 1933-1942 （in Chinese）. | |
| [8] | REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN： Towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2017， 39（6）： 1137-1149. |
| [9] | HE K M， ZHANG X Y， REN S Q， et al. Spatial pyramid pooling in deep convolutional networks for visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2015， 37（9）： 1904-1916. |
| [10] | 李鑫， 刘帅男， 杨桢， 等. 基于改进Cascade R-CNN的输电线路多目标检测［J］. 电子测量与仪器学报， 2021， 35（10）： 24-32. |
| LI X， LIU S N， YANG Z， et al. Multi-target detection of transmission lines based on improved cascade R-CNN［J］. Journal of Electronic Measurement and Instrumentation， 2021， 35（10）： 24-32 （in Chinese）. | |
| [11] | MA J Q， SHAO W Y， YE H， et al. Arbitrary-oriented scene text detection via rotation proposals［J］. IEEE Transactions on Multimedia， 2018， 20（11）： 3111-3122. |
| [12] | 赵其昌， 吴一全， 苑玉彬. 光学遥感图像舰船目标检测与识别方法研究进展［J］. 航空学报， 2024， 45（8）： 029025. |
| ZHAO Q C， WU Y Q， YUAN Y B. Progress of ship detection and recognition methods in optical remote sensing images［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（8）： 029025 （in Chinese）. | |
| [13] | DING J， XUE N， LONG Y， et al. Learning RoI transformer for oriented object detection in aerial images［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2019： 2849-2858. |
| [14] | XIE X， CHENG G， WANG J， et al. Oriented R-CNN for object detection［C］∥2021 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2021： 3520-3529. |
| [15] | YANG X， YAN J， FENG Z， et al. R 3 D e t ： Refined single-stage detector with feature refinement for rotating object［C］∥Proceedings of the AAAI Conference on Artificial Intelligence， 2021， 35（4）， 3163-3171. |
| [16] | 奉志强， 谢志军， 包正伟， 等. 基于改进YOLOv5的无人机实时密集小目标检测算法［J］. 航空学报， 2023， 44（7）： 327106. |
| FENG Z Q， XIE Z J， BAO Z W， et al. Real-time dense small object detection algorithm for UAV based on improved YOLOv5［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（7）： 327106 （in Chinese）. | |
| [17] | QURESHI M F， MUSHTAQ Z， REHMAN M Z U， et al. E2CNN： An efficient concatenated CNN for classification of surface EMG extracted from upper limb［J］. IEEE Sensors Journal， 2023， 23（8）： 8989-8996. |
| [18] | HAN J， DING J， XUE N， et al. ReDet： A rotation-equivariant detector for aerial object detection［C］∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021： 2786-2795. |
| [19] | WOO S， PARK J， LEE J Y， et al. CBAM： Convolutional block attention module［M］∥Computer Vision-ECCV 2018. Cham： Springer International Publishing， 2018： 3-19. |
| [20] | QIAO S Y， CHEN L C， YUILLE A. DetectoRS： Detecting objects with recursive feature pyramid and switchable atrous convolution［C］∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021： 10208-10219. |
| [21] | LIN T Y， DOLLáR P， GIRSHICK R， et al. Feature pyramid networks for object detection［C］∥2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2017： 936-944. |
| [22] | RAMACHANDRAN P， ZOPH B， LE Q V. Searching for activation functions［DB/OL］. arXiv preprint： 1710.05941， 2017. |
| [23] | GLOROT X， BORDES A， BENGIO Y. Deep sparse rectifier neural networks［C］∥Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics （AISTATS）. Fort Lauderdale： JMLR Workshop and Conference Proceedings， 2011： 315-323. |
| [24] | YANG X， ZHOU Y， ZHANG G F， et al. The KFIoU loss for rotated object detection［DB/OL］. arXiv preprint： 2201.12558， 2022. |
| [25] | YANG X， YANG X J， YANG J R， et al. Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence［DB/OL］. arXiv preprint： 2106.01883， 2021. |
| [26] | TAO X， ZHANG D P， WANG Z H， et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2020， 50（4）： 1486-1498. |
| [27] | VIEIRA E SILVA A L B， DE CASTRO FELIX H， SIM?ES F P M， et al. InsPLAD： A dataset and benchmark for power line asset inspection in UAV images［J］. International Journal of Remote Sensing， 2023， 44（23）： 7294-7320. |
| [28] | SELVARAJU R R， COGSWELL M， DAS A， et al. Grad-CAM： Visual explanations from deep networks via gradient-based localization［C］∥2017 IEEE International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2017： 618-626. |
| [29] | ZHANG J Q， LEI J， XIE W Y， et al. SuperYOLO： Super resolution assisted object detection in multimodal remote sensing imagery［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023， 61： 5605415. |
| [30] | LI Y X， HOU Q B， ZHENG Z H， et al. Large selective kernel network for remote sensing object detection［C］∥2023 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2023： 16748-16759. |
| [31] | WANG G， CHEN Y F， AN P， et al. UAV-YOLOv8： A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios［J］. Sensors， 2023， 23（16）： 7190. |
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