Porcelain insulator is an important component of power transmission systems

and its condition detection is essential to ensure safe operation of the power grid. Nevertheless

it is difficult for existing detection models to effectively solve the contradiction between detection accuracy and resource consumption. To address this issue

a high-precision lightweight insulator defect detection model (BCM-YOLO) based on an improved YOLOv8 is proposed. Firstly

bidirectional feature pyramid network (BiFPN)

with a simplified bidirectional information flow mechanism

is employed to replace the path aggregation network with feature pyramid network in YOLOv8 to alter the feature fusion mode

thereby reducing the model size. Secondly

a cross-stage partial Bottleneck with 2 convolutions partially replaced by a context-guided block (C2f_CG) structure with parameter sharing is designed using the improved context-guided block to optimise the cross-stage partial Bottleneck with 2 convolutions (C2f) modules

thus further decreasing the number of model parameters. Finally

multiscale dilated attention is introduced into the BiFPN network to enhance the perception ability of different scales of features to improve the detection performance. Experimental results indicate that compared to YOLOv8s

the BCM-YOLO model reduces the number of parameters by 50.5%

lowers floating-point operations by 31.3% and increases mean average precision at intersection over union = 0.5 (mAP0.5) by 2.8%. The proposed model not only improves detection accuracy but also decreases parameter counts

making it more suitable for deployment on edge devices.