An intelligent retrievable object‐tracking system with real‐time edge inference capability

Abstract

AbstractAn intelligent retrievable object‐tracking system assists users in quickly and accurately locating lost objects. However, challenges such as real‐time processing on edge devices, low image resolution, and small‐object detection significantly impact the accuracy and efficiency of video‐stream‐based systems, especially in indoor home environments. To overcome these limitations, a novel real‐time intelligent retrievable object‐tracking system is designed. The system incorporates a retrievable object‐tracking algorithm that combines DeepSORT and sliding window techniques to enhance tracking capabilities. Additionally, the YOLOv7‐small‐scale model is proposed for small‐object detection, integrating a specialized detection layer and the convolutional batch normalization LeakyReLU spatial‐depth convolution module to enhance feature capture for small objects. TensorRT and INT8 quantization are used for inference acceleration on edge devices, doubling the frames per second. Experiments on a Jetson Nano (4 GB) using YOLOv7‐small‐scale show an 8.9% improvement in recognition accuracy over YOLOv7‐tiny in video stream processing. This advancement significantly boosts the system's performance in efficiently and accurately locating lost objects in indoor home settings.