Vision-based Human Detection by Fine-Tuned SSD Models

Abstract

Human-robot interaction (HRI) and human-robot collaboration (HRC) has become more popular as the industries are taking initiative to idealize the era of automation and digitalization. Introduction of robots are often considered as a risk due to the fact that robots do not own the intelligent as human does. However, the literature that uses deep learning technologies as the base to improve HRI safety are limited, not to mention transfer learning approach. Hence, this study intended to empirically examine the efficacy of transfer learning approach in human detection task by fine-tuning the SSD models. A custom image dataset is developed by using the surveillance system in TT Vision Holdings Berhad and annotated accordingly. Thereafter, the dataset is partitioned into the train, validation, and test set by a ratio of 70:20:10. The learning behaviour of the models was monitored throughout the fine-tuning process via total loss graph. The result reveals that the SSD fine-tuned model with MobileNetV1 achieved 87.20% test AP, which is 6.1% higher than the SSD fine-tuned model with MobileNetV2. As a trade-off, the SSD fine-tuned model with MobileNetV1 attained 46.2 ms inference time on RTX 3070, which is 9.6 ms slower as compared to SSD fine-tuned model with MobileNetV2. Taking test AP as the key metric, SSD fine-tuned model with MobileNetV1 is considered as the best fine-tuned model in this study. In conclusion, it has shown that the transfer learning approach within the deep learning domain can help to protect human from the risk by detecting human at the first place.