Ibraheam, MaiLi, Kin FunGebali, Fayez2024-01-242024-01-2420232023Ibraheam, M., Li, K. F., & Gebali, F. (2023). MCFP-yolo animal species detector for embedded systems. Electronics, 12(24), 5044. https://doi.org/10.3390/electronics12245044https://doi.org/10.3390/electronics12245044http://hdl.handle.net/1828/15873Advances in deep learning have led to the development of various animal species detection models suited for different environments. Building on this, our research introduces a detection model that efficiently handles both batch and real-time processing. It achieves this by integrating a motion-based frame selection algorithm and a two-stage pipelining–dataflow hybrid parallel processing approach. These modifications significantly reduced the processing delay and power consumption of the proposed MCFP-YOLO detector, particularly on embedded systems with limited resources, without trading off the accuracy of our animal species detection system. For field applications, the proposed MCFP-YOLO model was deployed and tested on two embedded devices: the RP4B and the Jetson Nano. While the Jetson Nano provided faster processing, the RP4B was selected due to its lower power consumption and a balanced cost–performance ratio, making it particularly suitable for extended use in remote areas.enAttribution 2.5 Canadadeep learningconvolutional neural network (CNN)parallel processingpipeliningembedded devicedataflowanimal species detectionArticleDepartment of Electrical and Computer Engineering