Bird's Nest Target Image Detection Model for Transmission Lines in Complex Environments
To solve the problems of low accuracy,high false and missed detection rates,and the inaccurate positioning of birds'nests by power inspection drones in complex environments,an improved YOLO-nc-kd model,designed to detect birds'nests on transmission lines,is proposed herein based on the YOLOv5s model.An efficient Multi-scale Convolutional Feature Fusion Module(MCFFM)is designed to achieve efficient feature extraction at different scales,enabling the model to obtain richer and more diverse feature representations.An attention mechanism is introduced to enhance the ability of the backbone network on extracting features of the bird's nest in similar environmental backgrounds.A new localization loss function is designed to improve the localization accuracy and small object detection capability of bounding boxes.Knowledge distillation techniques are implemented to further improve the model accuracy.According to the experimental results,the accuracy,recall,and mean Average Precision(mAP)of the proposed YOLO-nc-kd model are improved by 7.3,5.6,and 4.9 percentage points,respectively,compared to those of the YOLOv5s model,indicating good detection performance for birds'nests on target images of transmission lines.
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