Abstract
Construction sites present significant potential safety hazards to the workers, with hand tools being a major source of injuries. This paper presents a Lightweight approach for Small Tools Detection (LSTD) method using a deep neural network for real-time detection of small construction tools. LSTD utilizes a lightweight backbone with Dynamic Feature Extraction, Accurate Separated Head, and Integrated Feature Fusion, reducing parameters by 73% and computations by 28% versus YOLOv5 while achieving 87.3% mean Average Precision (mAP) on challenging construction site datasets. Additional modules enhance detection recall and robustness to appearance variation and scale changes. Extensive experiments demonstrate LSTD's superior performance in misty conditions and illumination changes. With high accuracy in a compact 2.87 M parameter network, LSTD brings ubiquitous worker safety monitoring via edge devices closer to reality. The proposed model marks a significant advancement in improving safety in high-risk construction environments.
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