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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the importance of helmet detection in construction site management and the cost control of hardware facilities in engineering projects, this paper proposes a helmet detection approach based on Lighter and Tiny-YOLO ( LT-YOLO), a lightweight and improved version of the deep learning network Tiny-YOLO v3. The number of prediction layer is increased and an innovative R-DSC feature extraction module is introduced in LT-YOLO. The complexity of the model can be greatly reduced by R-DSC module without changing the size of the network inputs and outputs. The experimental results showed that LT-YOLO achieved an excellent balance between light weight and detection performance, reaching 59.3 mAP (mean average precision) and 59.4% Recall with only 3.5 M parameters. Because of very few parameters and very low computation, LT-YOLO has low dependence on high computing hardware, and is suitable for actual construction site safety management. LT-YOLO can greatly reduce the cost of enterprises and improve the level of construction safety management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)