Lightweight safety helmet detection algorithm based on improved YOLOv8n and partial convolution

Lightweight safety helmet detection algorithm for smart construction sites based on improved yolov8

A lightweight detection algorithm named GCW-YOLOv8, which improves detection accuracy by 1.4% while reducing parameter count by 34.4%, achieving a superior balance between accuracy and efficiency for smart construction site applications.

XLiteYOLOv12: a lightweight framework for real-time hazardous object detection

The YOLOv12 network is adopted as the baseline model and the ADown module is introduced to improve downsampling efficiency while maintaining lightweight performance, and the BN-CGLU is incorporated into the A2C2f module to enhance the model’s nonlinear representation capability.

Improved lightweight YOLOv11 for foreign object detection in transmission line inspection

A lightweight YOLOv11-based foreign object detector is designed, using StarNet to reconstruct the backbone, reducing redundant parameters and computational cost, and SDIoU is introduced for bounding box regression, which improves the localization of multi-scale targets.

LCV-YOLO: a lightweight and robust safety-helmet detection framework for complex underground mining environments

A lightweight and high-precision road defect detection method based on improved YOLO11

This paper proposes an improved lightweight YOLO11s algorithm to address the issues of excessive model complexity and poor performance when detecting small objects in road defect detection. By incorporating the StarNet architecture to reconstruct the backbone network, the method utilizes star-shaped operations to enhance fine-grained feature extraction while reducing computational overhead. It integrates the parameter-free attention mechanism SimAM, which employs three-dimensional weighted inference to enhance target responses and suppress background noise. Additionally, the Shape-IoU loss function is introduced to improve the regression accuracy of irregularly shaped objects through dynamic shape constraints. The experimental results demonstrate that the proposed algorithm attained an mAP50 of 87.02% on the RDD2022 dataset, indicating a 4.33% increase in recall. Concurrently, it achieved a 52.7% and 50.7% reduction in parameters and computational cost, respectively, thereby demonstrating a judicious balance between detection accuracy and lightweight deployment.

An Enhanced YOLOv8 Algorithm with C2f_Parnet for Vehicle and Pedestrian Detection

This paper proposes enhancements to the YOLOv8s model for autonomous driving object detection tasks. Firstly, a C2f Parnet module is designed based on the Parnet parallel network architecture to reduce network depth and computational latency. Secondly, GSConv is modified by employing dual 3 × 3 convolutions to decrease parameter count. Thirdly, L2-scored structural pruning is introduced to compress redundant channels. Finally, detection accuracy is improved using EIoU loss. Experiments on the KITTI dataset demonstrate that the improved model maintains a mAP50 of 0.917. The performance of this model is almost comparable to the original model (0.939), but with parameters reduced to 11 million and GFLOPs decreased to 26.2. Compared with other methods, L2 pruning achieved the highest mAP50-95 score of 0.753. In complex traffic environments, this model demonstrates robust real-time detection capabilities, can be flexibly deployed, and is a useful choice for lightweight detection in autonomous driving.