A Weakly Supervised Learning Framework Utilizing Enhanced Class Activation Map for Object Detection in Construction Sites

Abstract

Computer vision has emerged as a promising tool for improving safety at construction sites through automatic scene recognition. However, traditional approaches require significant labor-intensive and time-consuming efforts for annotations. Although weakly supervised learning has the advantage of localizing objects without location information, the conventional class activation map (CAM) technique struggles with small and linear object localization and background noise at construction sites. For effective scene recognition related to construction safety, the spatial relationships between precisely localized objects are crucial. Due to the limitations of traditional CAM techniques in localizing small and linear objects, using CAM for construction safety monitoring remains inaccurate. Therefore, this study proposes a weakly supervised learning approach with an improved CAM for enhancing object detection in construction sites. The improved CAM localizes objects of various scales and is robust against background interference. Spatial relationships between localized objects are employed to determine the status of scenes for construction safety monitoring. Experiment using datasets associated with falls from ladders (FFL) demonstrates that the improved CAM surpasses the traditional CAM in mIoU (mean intersection over union) for the object localization performance as well as in the accuracy and F1-Score for recognizing unsafe scenes. This demonstrates the robust potential of employing CAM as a weakly supervised learning strategy, underlining its substantial feasibility for preventing hazards in construction sites. The proposed framework can minimize annotation efforts, demonstrating the potential of CAM as a viable computer vision technique for efficiently detecting hazards at construction sites.

Keywords

• Computer vision
• weakly supervised learning
• class activation map
• scene recognition
• safety monitoring