Efficient Object Detection Model for Edge Devices

Deep learning-based object detection methods demonstrated promising results. In reality, most methods suffer while running on edge devices due to their extensive network architecture and low inference speed. Additionally, there is a lack of industrial scenarios in the existing person, helmet, and head detection datasets. This research presents an efficient tiny network (ETN) for object detection that can perform on edge devices with high inference speed. We take the YOLOv5s model as our base model. We compress the YOLOv5s object detection model and minimize the computation redundancy, and propose two lightweight C3 modules (MC3 and SC3). Additionally, we construct two novel datasets: H2 (consists of safety helmet and head) and Person104K (consists of person) that fill the gaps in the earlier datasets with various industrial scenarios. We implemented and tested our method on Person104K and H2 datasets and achieved about 50.6% higher inference speed than the original YOLOv5s without compromising the accuracy. On the Nvidia Jetson AGX edge device, ETN achieves 42% higher FPS compared to the original YOLOv5s. Code is available at https://github.com/mdhosen/ETN .