基于改进YOLOv11n的轻量化刮板链条检测算法

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中图分类号:TD528.3 文献标志码:A

Abstract:To address the issues of low detection accuracy,excessive model complexity,and high deployment and maintenance difficulty in existing scraper conveyor chain detection methods based on deep learningunder low illuminationconditions in coal mines,alightweight chain detectionalgorithm based on improved YOLOvlln—YOLO-Chain—was proposed.First,an image edge information enhancement module was constructed to optimize the C3k2 module of YOLOvlln,efectively extracting edge features from chain images.Then,a weighted Bidirectional Feature Pyramid Network (BiFPN) wasused to replace the neck network of YOLOvl1n,thereby effectively reducing the number of model parametersand lowering model complexity.Finaly,a lightweight detection head was introduced to capture subtle features of chain scale variations in complex underground scenarios, further reducing redundant parameters and model complexity, improving the detection performance of the lightweight model,and providing support for subsequent chain fault detection. Experimental results on a single-scenario scraper chain image dataset from a coal mine in Shanxi showed that,compared with the original YOLOv1ln model, YOLO-Chain improved the mAI accuracy by 3.7% ,while reducing the number of parameters and computational load by 35% and 10% ,respectively, and decreasing the model size by 31% . Compared with current mainstream models such as the YOLO series,SSD,Faster RCNN,and RT-DETRR18, YOLO-Chain also demonstrated advantages in multiple indicators. Experimental results on a multi-scenario chain image dataset collcted from multiple coal mines under complex working conditions such as low illumination, smoke interference, dust occlusion, and partial obstruction showed that the F1 -score and mAP@0.5 (20 of YOLO-Chain increased by 0.2% and 0.5% , respectively, compared with YOLOvl1n, with arithmetic speed increased by 8, demonstrating good applicability and generalization ability.

Key words: scraper conveyor; scraper chain; broken chain; deformed chain; YOLOvl1n; lightweight

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