| Author: | Wang, Xinran |
| Title: | Abnormal status detection of construction workers |
| Advisors: | Huang, Hailong (AAE) |
| Degree: | M.Sc. |
| Year: | 2023 |
| Department: | Department of Mechanical Engineering |
| Pages: | ix, 87 pages : color illustrations |
| Language: | English |
| Abstract: | The state of construction workers on construction sites is crucial for their physical health and work efficiency, among other aspects, and needs special attention. Due to long working hours, physical exertion, and harsh working conditions, workers easily experience fatigue during construction. When fatigued, workers tend to smoke to alleviate tiredness, which poses safety hazards such as fire accidents. Other workers may choose to make phone calls to relieve fatigue. These behaviors can cause distraction and safety hazards, threatening the workers’ safety. Therefore, detecting abnormal worker states becomes essential under the above circumstances. In this paper, we employ two parallel methods to comprehensively identify workers’ abnormal conditions. Firstly, we use the deep neural learning network VGG19 to classify the dataset into fatigue and non-fatigue, with fatigue including yawning and blinking; making phone calls and not making phone calls; smoking and not smoking. Then, we set the training, validation, and testing datasets. After deep learning, the training accuracy can reach above 0.95, and the prediction results have high probabilities in their corresponding categories, with excellent performance that can be applied in real-world scenarios. Secondly, we detect workers’ abnormal states in real-time. For fatigue detection, we use the dlib library, the Perclos fatigue algorithm, and 68 facial landmarks to detect yawning and blinking in real-time and perform counting. When the count exceeds a certain degree, a reminder will appear on the screen. For smoking and phone calls, we use the YOLOv5 object detection algorithm, which displays the location and confidence level of cigarettes or phones on the screen. When the confidence level is above 0.9, a reminder appears on the screen. Lastly, we apply the YOLOv7 algorithm for workers’ trajectory tracking and prediction, selecting linear prediction during the forecast. The accuracy is also excellent after calculation. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7479.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 9.04 MB | Adobe PDF | View/Open |
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