Fine-grained detection of damages on streetlights and power poles in complex scenarios
In response to challenges such as faint local features,significant intra-class variations,and a high model parameter count specific to pole damages,a Pole-YOLO algorithm designed for fine-grained detection of defects on streetlight and utility poles was proposed in this paper.Given that poles exhibit elongated cylindrical structures,the introduction of dynamic snake convolution addressed the challenge of model inattention towards faint local features,thereby alleviating recognition interruptions.For the diverse intra-class variations of damages,deformable convolutions were integrated into the C2f module to adapt to arbitrary-sized damage features,enhancing the capability of feature extraction.Furthermore,the WIOUv3 loss function was employed to enhance the learning capability for small target damages.Finally,the employment of distribution shift convolutions reduced the model parameter count.Comparative experiments conducted on a custom-made datasct for pole damages demonstrate that the Pole-YOLO method achieved precision,recall,F1-Score,and mean average precision mAP@0.5 of 84.2％,79.8％,81.9％,and 83.7％,respectively.This indicated its suitability for real-time detection of pole damages in urban scenarios.
upright pole disease detectionobject detectionfine-grained classificationstreet scene imagery
NETL
NSTL
万方数据
