MCPD‐YOLOv3: A Novel Lightweight Detection Model for Surgical Instruments in Laparoscopic Images

ABSTRACT Background Laparoscopic surgical instruments detection is necessary in computer‐aided minimally invasive surgery. Most current methods suffer from unsatisfied performance and low detection speed. Methods In this paper, a framework called MCPD‐YOLOv3 is proposed to balance the efficiency and effectiveness of laparoscopic surgical instruments detection. It effectively fuses feature maps using a parallel manner, and adopts various lightweight strategies to design a lightweight model. Besides, DIoU is employed to improve the recall performance. Results The proposed method achieved the mAP of 99.47% and 97.65% at 49.81 FPS for the ATLAS Dione and m2cai16‐tool‐locations datasets, respectively, with a compact model size of 12.4M and a low FLOPs count of 7.44G. Conclusion These results highlight that MCPD‐YOLOv3 excels in high detection performance and rapid response. The model's efficiency in parameter size and FLOPs demonstrates its suitability for applications requiring rapid processing and precise detection, making it a valuable tool for real‐time surgical instrument detection in challenging environments.