Workers and Safety Helmets Detection in Day and Night Scenes based on improved YOLOv5

[1] Li Z, Liu F, Yang W, et al. A survey of convolutional neural networks: analysis, applications, and prospects[J]. IEEE transactions on neural networks and learning systems, 2021. Search in Google Scholar

[2] Akinosho T D, Oyedele L O, Bilal M, et al. Deep learning in the construction industry: A review of present status and future innovations[J]. Journal of Building Engineering, 2020, 32: 101827. Search in Google Scholar

[3] Paneru S, Jeelani I. Computer vision applications in construction: Current state, opportunities & challenges[J]. Automation in Construction, 2021, 132: 103940. Search in Google Scholar

[4] Zhou F, Zhao H, Nie Z. Safety helmet detection based on YOLOv5[C]//2021 IEEE International conference on power electronics, computer applications (ICPECA). IEEE, 2021: 6–11. Search in Google Scholar

[5] Rebholz, F. E., Al-Kaisy, A. F., Nassar, K., Liu, L., Soibelman, L., El-Rayes, K., Bradley University. Dept. of Civil Engineering and Construction, & University of Illinois at Urbana-Champaign. Dept. of Civil and Environmental Engineering. (2004). Nighttime construction: Evaluation of construction operations : final report. (ITRC FR 00/01-5). https://rosap.ntl.bts.gov/view/dot/16121 Search in Google Scholar

[6] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580–587. Search in Google Scholar

[7] Uijlings J R R, Van De Sande K E A, Gevers T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2): 154–171. Search in Google Scholar

[8] Hearst M A, Dumais S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and their applications, 1998, 13(4): 18–28. Search in Google Scholar

[9] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904–1916. Search in Google Scholar

[10] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories[C]//2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06). IEEE, 2006, 2: 2169–2178. Search in Google Scholar

[11] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28. Search in Google Scholar

[12] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779–788. Search in Google Scholar

[13] Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning (ICML), pp 448–456 Search in Google Scholar

[14] Wagstaff K, Cardie C, Rogers S, Schr?dl S (2001) Constrained k-means clustering with background knowledge. In: International conference on machine learning (ICML), pp 577–584 Search in Google Scholar

[15] He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778 Search in Google Scholar

[16] Lin TY, Dollar P, Girshick R, He KM, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 936–944 Search in Google Scholar

[17] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263–7271. Search in Google Scholar

[18] Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018. Search in Google Scholar

[19] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020. Search in Google Scholar

[20] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision (ECCV), pp 21–37 Search in Google Scholar

[21] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014. Search in Google Scholar

[22] Xiao Y, Tian Z, Yu J, et al. A review of object detection based on deep learning[J]. Multimedia Tools and Applications, 2020, 79(33): 23729–23791. Search in Google Scholar

[23] Son H, Choi H, Seong H, et al. Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks[J]. Automation in Construction, 2019, 99: 27–38. Search in Google Scholar

[24] Kang K S, Cho Y W, Jin K H, et al. Application of one-stage instance segmentation with weather conditions in surveillance cameras at construction sites[J]. Automation in Construction, 2022, 133: 104034. Search in Google Scholar

[25] He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2961–2969. Search in Google Scholar

[26] Xie Z, Liu H, Li Z, et al. A convolutional neural network based approach towards real-time hard hat detection[C]//2018 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, 2018: 430–434. Search in Google Scholar

[27] Shen J, Xiong X, Li Y, et al. Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning[J]. Computer‐Aided Civil and Infrastructure Engineering, 2021, 36(2): 180–196. Search in Google Scholar

[28] Wang L, Xie L, Yang P, et al. Hardhat-wearing detection based on a lightweight convolutional neural network with multi-scale features and a top-down module[J]. Sensors, 2020, 20(7): 1868. Search in Google Scholar

[29] Zhang C, Tian Z, Song J, et al. Construction worker hardhat-wearing detection based on an improved BiFPN[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 8600–8607. Search in Google Scholar

[30] Tan M, Pang R, Le Q V. Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10781–10790. Search in Google Scholar

[31] Wang W, Peng Y, Cao G, et al. Low-illumination image enhancement for night-time UAV pedestrian detection[J]. IEEE Transactions on Industrial Informatics, 2020, 17(8): 5208–5217. Search in Google Scholar

[32] Liu S, Wu H, Rahman M A, et al. Enhancement of low illumination images based on an optimal hyperbolic tangent profile[J]. computers & electrical engineering, 2018, 70: 538–550. Search in Google Scholar

[33] Danielyan A, Katkovnik V, Egiazarian K. BM3D frames and variational image deblurring[J]. IEEE Transactions on image processing, 2011, 21(4): 1715–1728. Search in Google Scholar

[34] Xiao B, Lin Q, Chen Y. A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement[J]. Automation in Construction, 2021, 127: 103721. Search in Google Scholar

[35] Leung H K, Chen X Z, Yu C W, et al. A deep-learning-based vehicle detection approach for insufficient and nighttime illumination conditions[J]. Applied Sciences, 2019, 9(22): 4769. Search in Google Scholar

[36] Kang K S, Cho Y W, Jin K H, et al. Application of one-stage instance segmentation with weather conditions in surveillance cameras at construction sites[J]. Automation in Construction, 2022, 133: 104034. Search in Google Scholar

[37] Jhong S Y, Chen Y Y, Hsia C H, et al. Nighttime object detection system with lightweight deep network for internet of vehicles[J]. Journal of Real-Time Image Processing, 2021, 18(4): 1141–1155. Search in Google Scholar

[38] Chen Y, Shin H. Pedestrian detection at night in infrared images using an attention-guided encoder-decoder convolutional neural network[J]. Applied Sciences, 2020, 10(3): 809. Search in Google Scholar

[39] Xiao B, Kang S C. Development of an image data set of construction machines for deep learning object detection[J]. Journal of Computing in Civil Engineering, 2021, 35(2): 05020005. Search in Google Scholar

[40] GitHub. YOLOv5-Master. 2021. Available online: https://github.com/ultralytics/yolov5. Search in Google Scholar

[41] Zheng Z, Wang P, Liu W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 12993–13000. Search in Google Scholar

[42] Zhang Y F, Ren W, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146–157. Search in Google Scholar

[43] Rosenfeld A, Thurston M. Edge and curve detection for visual scene analysis[J]. IEEE Transactions on computers, 1971, 100(5): 562–569. Search in Google Scholar

[44] Bodla N, Singh B, Chellappa R, et al. Soft-NMS--improving object detection with one line of code[C]//Proceedings of the IEEE international conference on computer vision. 2017: 5561–5569. Search in Google Scholar

[45] Borji A, Itti L. State-of-the-art in visual attention modeling[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 35(1): 185–207. Search in Google Scholar

[46] Mnih V, Heess N, Graves A. Recurrent models of visual attention[J]. Advances in neural information processing systems, 2014, 27. Search in Google Scholar

[47] Li X, Wang W, Hu X, et al. Selective kernel networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 510–519. Search in Google Scholar

[48] Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7794–7803. Search in Google Scholar

[49] Lin Z, Feng M, Santos C N, et al. A structured self-attentive sentence embedding[J]. arXiv preprint arXiv:1703.03130, 2017. Search in Google Scholar

[50] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132–7141. Search in Google Scholar

[51] MacQueen J. Classification and analysis of multivariate observations[C]//5th Berkeley Symp. Math. Statist. Probability. Los Angeles LA USA: University of California, 1967: 281–297. Search in Google Scholar

[52] Arthur D, Vassilvitskii S. K-means++ the advantages of careful seeding[C]//Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 2007: 1027–1035. Search in Google Scholar

[53] Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data[J]. IEEE intelligent systems, 2009, 24(2): 8–12. Search in Google Scholar

[54] Joffrey L, Taghi M K, Richard B. A survey on addressing high-class imbalance in big data. J Big Data (2018) 5: 42[J]. Search in Google Scholar

[55] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. 2012 Advances in Neural Information Processing Systems (NIPS)[J]. Neural Information Processing Systems Foundation, La Jolla, CA, 2012. Search in Google Scholar

[56] Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning[J]. Journal of big data, 2019, 6(1): 1–48. Search in Google Scholar

[57] Kang G, Dong X, Zheng L, et al. Patchshuffle regularization[J]. arXiv preprint arXiv:1707.07103, 2017. Search in Google Scholar

[58] Zhong Z, Zheng L, Kang G, et al. Random erasing data augmentation[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 13001–13008. Search in Google Scholar

[59] DeVries T, Taylor G W. Dataset augmentation in feature space[J]. arXiv preprint arXiv:1702.05538, 2017. Search in Google Scholar

[60] Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[J]. arXiv preprint arXiv:1412.6572, 2014. Search in Google Scholar

[61] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139–144. Search in Google Scholar

[62] Zhang D, Han J, Cheng G, et al. Weakly supervised object localization and detection: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(9): 5866–5885. Search in Google Scholar