Zhongyuan Zhang, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

Wen-Tsai Sung¹, Sung-Jung Hsiao^2,*, Chung-Yen Hsiao¹

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1223-1240, 2022, DOI:10.32604/iasc.2022.020746

Abstract This research is based on the system architecture of Edge Computing in the AIoT (Artificial Intelligence & Internet of Things) field. In terms of receiving data, the authors proposed approach employed the camera module as the video source, the ultrasound module as the distance measurement source, and then compile C++ with Raspberry Pi 4B for image lane analysis, while Jetson Nano uses the YOLOv3 algorithm for image object detection. The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load… More >