Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT

TLDR

Cloud video surveillance has become a key topic in smart IoT, yet real‑time multitarget detection remains challenging due to complex environments and limited edge resources. This study targets real‑time multitarget detection in smart IoT systems. The authors propose A‑YONet, a lightweight deep network that fuses YOLO and MTCNN with a pre‑adjusted anchor scheme and multilevel feature fusion for efficient edge‑cloud deployment. Experiments on a public and a real‑world surveillance dataset show that A‑YONet improves training efficiency and detection precision for multitarget scenarios in smart IoT.

Abstract

Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video surveillance (CVS) has become a hotly discussed topic, especially when facing the requirement of real-time analysis in smart applications. Object detection usually plays an important role for environment monitoring and activity tracking in surveillance system. The emerging edge-cloud computing paradigm provides us an opportunity to deal with the continuously generated huge amount of surveillance data in an on-site manner across IoT systems. However, the detection performance is still far away from satisfactions due to the complex surveilling environment. In this study, we focus on the multitarget detection for real-time surveillance in smart IoT systems. A newly designed deep neural network model called A-YONet, which is constructed by combining the advantages of YOLO and MTCNN, is proposed to be deployed in an end-edge-cloud surveillance system, in order to realize the lightweight training and feature learning with limited computing sources. An intelligent detection algorithm is then developed based on a preadjusting scheme of anchor box and a multilevel feature fusion mechanism. Experiments and evaluations using two data sets, including one public data set and one homemade data set obtained in a real surveillance system, demonstrate the effectiveness of our proposed method in enhancing training efficiency and detection precision, especially for multitarget detection in smart IoT application developments.