Object detection based on deep learning techniques in resource-constrained environment for healthcare industry

Advanced technologies and algorithms such as the Internet of Things (IoT), computer vision (CV), and deep learning are widely used in the healthcare industry to enhance global med-ical care. Internet of Things (IoT) has the potential to be limitless due to increased network agility, integrated artificial intelligence (AI), and the ability to deploy and automate systems. Embedded systems playa vital role in IoT due to real-time computing, low power consumption, and low maintenance cost. Object detection is a computer vision technique that aims to process and identify certain objects such as people, cars, animals, or buildings in digital images or videos. The goal of object detection is to develop computational models for computer vision applications. Recently, rapid advancement in deep learning techniques accelerated the momentum of object detection. In this study, we proposed a mechanism to perform object detection based on deep learning techniques in resource-constrained IoT devices. Due to limited computational powers in embedded systems, the performance of deep learning algorithms is not good enough. To achieve this, we compressed the video using a codec and streamed it to the amazon cloud for object detection. Video codec was used to uncompress the video in its original format so that there is no loss of video quality. A pre-trained YOLO model was deployed for object detection in medical images. The output is sent to the client using a lightweight protocol for data communication. Results indicate that the proposed mechanism worked well in a resource-constrained environment without compromising accuracy and time.