International Journal Of Imaging

Abstract

DEEP LEARNING FOR PPE DETECTION: ENHANCING WORKPLACE SAFETY WITH SSD

Author: B. Deepali, B. Srujana , N. Bhoomi Reddy, Suresh Talwar

Abstract:

Personal Protective Equipment (PPE) serve as crucial components for engineers in the construction industry and other sectors, ensuring the safety and well-being of workers. An effective object detection system plays a pivotal role in identifying and monitoring the usage of PPE. This research focuses on testing a detection system utilizing a dataset comprising 132 scenarios from construction sites. The results indicate the system's successful detection of PPE with a high level of confidence. Evaluation metrics such as mAP50(B), mAP50-95(B), precision(B), and recall(B) were employed to assess system performance. The testing yielded mAP50(B) of 0.768, mAP50-95(B) of 0.516, precision(B) of 0.831, and recall(B) of 0.693. These outcomes demonstrate that the PPE detection system, utilizing the SSD algorithm, exhibits satisfactory performance in recognizing and predicting PPE objects. However, there is room for improvement and further development, especially in addressing challen