Research on dense small target scrap steel type recognition algorithm based on YOLO-SSNP

Scrap can lower production costs for metallurgical companies and enhance environmental sustainability. It is a recyclable resource and an essential substitute for iron ore as a raw material. The accuracy of classification is hampered by the limits of current scrap steel recognition techniques, which include limited category coverage, small-target detection capabilities, and resistance to background interference. This research suggests YOLO-SSNP, a dense, small-target scrap steel material detection algorithm created by improving the YOLOv5 framework, as a solution to these issues. To improve the model’s ability to extract fine-grained features, a small-target detection head is first included. Second, to maximize feature representation while preserving model compactness, GSConv and VoVGSCSP are used to build a Slim-Neck module, which replaces conventional Conv and C3 modules in parts of the Neck layer. Finally, Soft-NMS replaces traditional NMS to enhance recognition accuracy for occluded and overlapping targets. The model is trained and evaluated on a self-constructed scrap steel image dataset and compared against several mainstream detection algorithms. Experimental results demonstrate thatYOLO-SSNP offers superior accuracy and model compactness, with a precision (P) value of 97.9% and an mAP of 89.7%. It highlights the model’s efficacy in precisely and efficiently identifying the different types of scrap steel material by improving the mAP value by 28.6% and 17.8%, respectively, over earlier methods.