Lightweight safety helmet detection algorithm based on improved YOLOv8n and partial convolution
TL;DR
The results prove that the proposed Faster-YOLOv8 method finds a much better balance between model size and performance, which is a significant improvement over other lightweight option like GhostNet.
Abstract
Detecting safety helmets is a vital task for industrial management and Internet of Things (IoT) systems. The problem, however, is that deploying high-performance detection models on edge devices is difficult. These devices often struggle with limited resources, leading to high computational redundancy and memory latency. To solve this efficiency issue without losing accuracy, we propose Faster-YOLOv8. This is a lightweight helmet detection algorithm built on Partial Convolution (PConv). Our key move is reconstructing the backbone network: we replaced the original bottleneck layers with a new C2f Faster module. This design cuts down on unnecessary calculations but keeps the feature diversity intact. We ran extensive tests on a self-built safety helmet dataset, and the results prove that our method finds a much better balance between model size and performance. Specifically, Faster-YOLOv8 reduces the parameter count by about 27% (down to 2.19M) and FLOPs by 23% compared to the baseline. At the same time, it maintains a high mAP@0.5 of 87.7%, which is a significant improvement over other lightweight option like GhostNet.