PGDIG-YOLO: a lightweight method for airport runway foreign object detection

Abstract

Aiming at the frequent misdetection and omission in the detection process of airport runway foreign object debris (FOD) and the difficulty of deploying the detection algorithm to embedded devices, we propose a lightweight FOD detection method called PGDIG-YOLO based on the improvement of YOLOv8n. First, a detection layer for detecting small-size objects is added and a large target detection layer is deleted to enhance the network’s ability to sense small-sized objects. Second, a dilation-wise residual module is introduced in the segmentation domain, and the C2FD module is proposed, which effectively solves the problem of misdetection and missed detection of FOD on airport runways. Third, the inner-WMPDIoUv3 is designed to replace the CIoU as a loss function to improve the regression accuracy of the detection frame. Finally, the model is pruned using the Group_sl method, which reduces the amount of computation, compresses the model size, and improves the model inference speed. The experimental results on the homemade dataset FOD-Z show that, compared with the benchmark model YOLOv8n, the model volume and computation of the PGDIG-YOLO network are only 6.6% and 44.4% of the original network, and the accuracy and recall are improved by 1.1% and 3.8%, respectively. Meanwhile, the mAP@0.5, mAP@0.75, and mAP@0.5:0.95 are increased to 99.1%, 93.7%, and 85.6%, respectively. Deploying PGDIG-YOLO to the NVIDIA Jetson Xavier NX 16 GB embedded device, the detection speed reaches 42 FPS, which can realize real-time FOD detection.