CAMDet: CAM-based objection detection for non-crowded views from moving IoT devices

The integration of Internet of Things (IoT) and Artificial Intelligence (AI) brings us AIoT that delivers the capabilities of object detection, device localization, object tracking and re-identification on moving IoT devices to control robots/drones for smart human-machine interactions. Among the tasks, efficient objection detection plays an importance role since it acts as the foundation of many other vision-based IoT applications. One main challenge is to locate target objects fast and accurate. The paper presents the CAMDet technology that utilizes Class Activation Map (CAM) to reduce the convolution blocks and the enormous candidate bounding boxes in the detection-head stage. We have designed CAMDet and integrate it with other backbone networks. CAMDet is shown to be 2.1-2.7 times faster than the popular Tiny-YOLO/SSD methods in non-crowded scenarios when using the same backbone and feature pyramid structure. The performance study shows that our proposed methods are very attractive for real time object detection on moving IoT devices.