TL;DR
This work proposes a detection framework called distillation alignment YOLO (DA-YOLO) for PPE detection and introduces a teacher–student distillation framework with consistency constraints across predictions and high-order features extracted from baseline that enables the student model to achieve strong generalization while maintaining low computational cost.
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning...
This paper presents a comprehensive study on deep learning-based personal protective equipment (PPE) detection for real-time construction safety monitoring by applying four recent YOLO-based object detection architectures on a unified dataset with 43,986 images and 14 PPE-related classes. The investigate the detection...
To address the challenges of weak feature representation, background interference, and missed small-target detections in identifying subtle surface damages on highly reflective refrigerator doors during production, this paper proposes a lightweight detection algorithm, YOLOv8-FD, based on the YOLOv8n architecture. Firs...
Illegal waste dumping poses significant environmental and public health challenges worldwide, requiring automated surveillance systems for detection and prevention. This paper presents our solution for the IWDD 2026 Contest, addressing the dual challenge of detecting illegal dumping events in surveillance videos and lo...
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.
Experiments show that, after applying the proposed knowledge distillation method, the inference latency is reduced from 235 ms to a minimum of 26 ms, which is better than DistilBERT’s 35 ms, verifying the efficiency and practicality of the lightweight model in resource-constrained scenarios involving power-risk identif...
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