MCFP-YOLO animal species detector for embedded systems
Date
2023
Authors
Ibraheam, Mai
Li, Kin Fun
Gebali, Fayez
Journal Title
Journal ISSN
Volume Title
Publisher
Electronics
Abstract
Advances 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.
Description
Keywords
deep learning, convolutional neural network (CNN), parallel processing, pipelining, embedded device, dataflow, animal species detection
Citation
Ibraheam, M., Li, K. F., & Gebali, F. (2023). MCFP-yolo animal species detector for embedded systems. Electronics, 12(24), 5044. https://doi.org/10.3390/electronics12245044