Object detection and recognition techniques require large image datasets, memory, a workstation with specific graphics processing capability to train the algorithm and might have high power consumption. Embedded platforms on the other hand are characterized by portability, low power consumption and space, and energy resources making the deployment of such algorithms on them difficult. In order to overcome these drawbacks, cloud-based processing embedded system for object detection and recognition is proposed in this work. The system consists of an image acquisition device set up using embedded board and camera to capture, process and send images to the remote computer via cloud storage platform. This cloud platform serves as an interface between the embedded board and the remote computer. The detection algorithm of Faster R-CNN is executed on the remote computer and is trained and validated with 3000 images obtained from ImageNet. The training of the algorithm aims to detect five classes of object. The proposed system was validated off-line and have achieved a mean Average Precision (mAP) of 0.67. The performance of entire system procedure took about 45 seconds and have obtained an average confidence score of 0.86.
