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

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

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.

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.

YOLOv8 vehicle detection method based on lightweight coordinate attention

To address the challenges encountered in vehicle detection within complex traffic scenarios, such as diverse features, strong background interference, and deployment constraints on edge devices, a lightweight vehicle detection method based on YOLOv8 improved with the Coordinate Attention mechanism is proposed. In this method, the Coordinate Attention module is introduced into the YOLOv8s backbone network to reconstruct the C2f structure. By embedding positional information, the model enhances its capability to extract key features, suppressing complex background interference while maintaining its lightweight characteristics. Experiments conducted on a dataset comprising 17,428 images of 17 vehicle classes demonstrate that the improved model achieves a precision of 90.4% and an mAP@0.5 of 91.5%. For vehicle types with regular structures and large volumes, such as four-wheel small trucks and six-wheel medium trucks, the detection accuracy approaches 99%, and the detection confidence remains stable above 0.8 under complex lighting and background interference conditions. This method effectively controls model complexity while maintaining high detection accuracy, thereby satisfying the application requirements for real-time vehicle detection on edge devices such as intelligent driving recorders.

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

Research on lightweight detection of maritime ship targets based on iGF-YOLOv11

To address the difficulty of effectively detecting maritime ship targets on mobile devices with limited computing resources under interference from illumination variations and wave disturbances, this paper proposes a lightweight ship detection model, termed iGF-YOLOv11, based on YOLOv11. Firstly, the GhostConv module is used to replace the standard convolution in the backbone network to reduce the model complexity and computational load; secondly, the iEMA attention module that integrates the advantages of iRMB and EMA mechanisms is introduced in the neck network to enhance the model's ability to extract key features of ships; moreover, a P2 detection head is added to improve the perception performance of small ship targets. Experimental results show that the improved model maintains high efficiency while significantly improving the detection accuracy. The proposed method achieves a balance between detection accuracy and lightweighting, providing a feasible solution for the deployment and operation of detection models on mobile devices.

A lightweight detection method for defects of transmission line insulators and accessories based on improved YOLOv11n

Insulators and their accessories are core components ensuring the safe operation of transmission lines. Defect detection for them is confronted with challenges such as small targets, complex backgrounds and difficulty in fitting the irregular boundaries of defects, while traditional lightweight algorithms suffer from insufficient detection performance. To tackle these challenges, this work presents a lightweight detection algorithm built upon the improved YOLOv11n: the DCNv3 (Deformable Convolution v3) is embedded into the backbone network to replace the traditional convolutional layers, enhancing the ability to extract features of irregular defects; the Inner-WIoU(Inner-Weighted Intersection over Union) loss function is employed to refine the bounding box regression. All experiments are conducted on a self-built dataset with 7,972 images and 7 defect categories. The proposed method achieves a mAP@50 of 86.4%, 3.2 percentage points higher than the baseline YOLOv11n, and outperforms mainstream lightweight detectors and small-object-oriented detectors. Meanwhile, the algorithm retains the lightweight advantage and meets the needs of on-site real-time inspection. The model maintains a lightweight structure with only 2.6 M parameters and achieves an inference speed of 38 FPS on NVIDIA RTX 4080 Super, meeting real-time inspection requirements. Meanwhile, the algorithm retains the lightweight advantage and can meet the requirements of on-site real-time inspection. Since validation is limited to a self-constructed dataset, cross-scene testing and public benchmark evaluation will be conducted in future work to further verify the model’s robustness and generalization in real transmission-line inspection scenarios.