High-Resolution Remote Sensing Image Object Detection System for Small Unmanned Aerial Vehicles Based on MPSOC

With the maturation of remote sensing, the applications of small unmanned aerial vehicles are rapidly expanding. Efficient image object detection algorithms have become crucial for information extraction in unmanned aerial vehicles. To meet this demand, an improved YOLOv5s algorithm was developed and deployed within a multi-processor system to optimize the performance of object detection in high-resolution remote sensing images captured by small unmanned aerial vehicles. Through adjustments to the structure and parameters of YOLOv5s, the algorithm was enhanced to improve object recognition capabilities in high-resolution remote sensing imagery. Experimental results demonstrated that the improved YOLOv5s (I-YOLOv5s) algorithm effectively mitigates interference from shadows and other external factors, enabling precise identification of objects. During training, I-YOLOv5s exhibited faster convergence, reaching optimal status after approximately 176 iterations. In performance evaluation, the algorithm achieved F1 and Recall values of 0.92 and 0.94, respectively, significantly outperforming single-shot multibox detectors. I-YOLOv5s attained a maximum average precision of 0.96, markedly higher than comparative algorithms, with its Loss value reduced to a mere 0.06. The introduction of this enhanced algorithm not only enhances the accuracy and efficiency of object detection but also profoundly advances the further application of unmanned aerial vehicles in fields such as environmental monitoring, traffic management, and disaster assessment.

@article{Xia2024,
  title     = {High-Resolution Remote Sensing Image Object Detection System for Small Unmanned Aerial Vehicles Based on MPSOC},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hui Xia},
  doi       = {10.14569/IJACSA.2024.0150673},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150673}
}