Abstract
Automatic detection of workers wearing safety helmets at the construction site is essential for safe production. Aiming at the problem of low recognition rate caused by factors such as background and light in the automatic detection of safety helmets using traditional machine learning methods, this paper proposes an object detection framework that combines Online Hard Example Mining (OHEM) and multi-part combination. In our framework, we first use the multi-scale training and the increasing anchors strategies to enhance the robustness of the original Faster RCNN algorithm to detect different scales and small object. Then, the OHEM is to optimize the model to prevent the imbalance of positive and negative samples. Finally, the person wearing the helmet and its parts (helmet and person) are detected by improved Faster RCNN. The multi-part combination method uses the geometric information of the detection objects to determine if a worker is wearing a helmet. Experiments show that compared with the original Faster RCNN, the detection accuracy is increased by 7%. It also has better detection performance for partial occlusion and different-size objects, showing good generalization and robustness.
Highlights
Various risk factors threaten safety of workers due to complex environment in chemical plants, power substations and construction sites
Due to the challenge of detecting small helmet targets on the construction site, this paper proposes an object detection framework based on combining Online Hard Example Mining (OHEM) and multi-part combination
We propose a framework for safety helmet wearing detection by improving the Faster RCNN
Summary
Various risk factors threaten safety of workers due to complex environment in chemical plants, power substations and construction sites. People working in such places must wear safety helmets to protect them from being struck by falling objects and falling to lower level [3]. Due to the challenge of detecting small helmet targets on the construction site, this paper proposes an object detection framework based on combining Online Hard Example Mining (OHEM) and multi-part combination. The multi-component combination method is utilized to calculate the belonging relationship of the components to detect the wearing of the safety helmet accurately. (3) The framework can automatically detect the wearing of safety helmets in different construction site scenarios. This method can obtain better detection accuracy and robustness than the other state-of-the-art methods.
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