A Lightweight Track Feature Detection Algorithm Based on Element Multiplication and Extended Path Aggregation Networks

Aiming at the problems of excessive computational load, insufficient real-time performance, and an excessive amount of model parameters in track inspection, this paper proposes a lightweight track feature detection module (YOLO-LWTD) based on YOLO11n: first, the StarNet module is integrated into the backbone network, and its elemental multiplication operation is utilized to enhance the feature characterization capability; second, in the neck part, a lightweight extended path aggregation network reconstructs the feature pyramid information flow paths by combining with the C3K2-Light module to enhance the efficiency of the multi-scale feature fusion; finally, in the head part, a lighter and more efficient detection header, Detect-LADH, is used to reduce the feature decoding complexity. Experimental validation showed that the improved model outperforms the benchmark model in precision, recall, and mean average precision (MAP) by 0.5%, 2.0%, and 0.8%, respectively, with an inference speed of 163 FPS (a 38.1% improvement). The model volume is compressed to 1.5 MB (a 71.1% lightweight rate). This provides an energy-efficient solution for lightweight track detection tasks geared towards embedded deployment or real-time processing.