YOLOv8-MCDE: A Lightweight Model for Detecting Small Instruments in Complex Environments from Inspection Robots’ Perspective

This paper addresses the challenges of equipment inspection in complex substation environments by proposing a lightweight small object detection algorithm, YOLOv8-MCDE, specifically designed for instrument recognition and suitable for deployment on inspection robots. Through model structure optimization, the proposed method significantly enhances both the small object detection performance and real-time efficiency of instrument detection on edge computing devices. YOLOv8-MCDE adopts the lightweight MobileNetV3 architecture as its backbone, effectively reducing model complexity and improving operational efficiency. The neck integrates a CNN-based Cross-scale Feature Fusion (CCFF) algorithm, which further lowers computational overhead while enhancing detection capability for small objects. In addition, a Deformable Large Kernel Attention (D-LKA) mechanism is integrated to increase the model’s sensitivity to small objects within complex backgrounds. The conventional CIOU loss function is also replaced with the more efficient EIOU loss function, significantly improving bounding box localization accuracy and accelerating model convergence. Experimental results demonstrate that YOLOv8-MCDE achieves a Precision of 92.80% and an mAP50 of 91.36%, representing improvements of 2.38% and 1.27%, respectively, compared to the original YOLOv8. Furthermore, the proposed algorithm reduces FLOPs by 37.68% and model size by 36%. These enhancements substantially reduce computational resource demands while significantly improving the real-time detection capabilities and small object recognition performance of inspection robots operating in complex environments.