Personal Protection Equipment detection system for embedded devices based on DNN and Fuzzy Logic

The large extension and complex structure of most industrial and construction areas very often make it unfeasible or inconvenient for human operators to constantly survey all the workers to detect those who do not properly wear their Personal Protection Equipment (PPE) devices. However, such a detection is of utmost importance to reduce the number of worker injuries. Consequently, the adoption of a computer vision system based on Deep Neural Networks (DNNs) that performs PPE detection by analysing the video streams from surveillance cameras is an appealing option. For instance, smart video cameras placed in the workplace might process the video frames at run-time and trigger alarms whenever they detect workers not correctly wearing PPE devices. However, in order to be sufficiently accurate, DNN-based object detection requires a high computational power that is difficult to embed in cameras. Moreover, DNN training has to be done on a large dataset with thousands of labeled image samples, and therefore the creation of a customized DNN to detect special PPE devices requires a huge effort in finding and labeling images to train the network. This paper proposes a PPE detection framework that combines DNN-based object detection with human judgement through fuzzy logic filtering. The proposed framework runs in near real-time on embedded devices and can be trained with a low number of images (i.e., few hundreds), still providing good accuracy results.

Automated summary

This paper introduces a PPE detection framework that combines deep neural networks and fuzzy logic filtering, which can run in real-time on embedded devices and achieve good accuracy results with training on a low number of images.