A Novel High-Precision and Low-Latency Abandoned Object Detection Method Under the Hybrid Cloud-Fog Computing Architecture

Abandoned object Detection (Aod) is of critical importance in the field of public safety. However, the demand on detection accuracy and latency hinders the development of ubiquitous Aod in safety protection, especially for some surveillance devices with relatively low-computational capacity. To this end, a novel high-precision and low-latency Aod method under the hybrid cloud-fog computing architecture is proposed in this article. To be specific, a YOLO-various hidden (YOLO-VH) Aod network model, which is integrated with an efficient dynamic convolution-based ghost module and a Haar wavelet-based downsampling convolution module, is presented to improve the detection accuracy of Aod. In addition, a flexible task offloading strategy is proposed to offload some of the Aod tasks based on the expectation cursor, which is designed to determine the local optimal offloading amount at different times. Finally, extensive experiments are conducted to verify the performance of our proposal through simulations. Our proposal exhibits a reduction of approximately 5.31 million parameters and 30.4 GFLOPs in computation compared with YOLOv9, while demonstrating performance improvements of 25.0% and 38.8% relative to cloud and fog computing, respectively. Furthermore, the total latency for image acquisition, task offloading, and task processing has been observed to be approximately 60% and 15% lower than cloud and fog computing, respectively.