Abstract: Illegal mining activities present significant environ-mental, economic, and safety challenges, particularly in remote and under-monitored regions. Traditional surveillance methods are often inefficient, labor-intensive, and unable to provide real-time insights. To address this issue, this study proposes a computer vision-based solution leveraging the state-of-the-art YOLOv11 Nano and Small models, fine-tuned for the detection of illegal mining activities. A specific dataset comprising aerial and ground-level images of mining sites was curated and annotated to train the models for identifying unauthorized excavation, equipment usage, and human presence in restricted zones. The proposed system integrates the hardware-software design of YOLOv11 on the PynqZ1 FPGA, offering a high-performance, low-latency, and energy-efficient solution suitable for real-time monitoring in resource-constrained environments. This hardware-accelerated approach combines FPGA’s parallel processing capabilities with the lightweight deep learning models, enabling efficient deployment for automated illegal mining detection. By providing a scalable, real-time monitoring tool, this work contributes to the development of automated enforcement tools for the mining industry, ensuring better control and surveillance of mining activities. To validate the efficiency of deep learning deployment on edge devices, YOLOv11n was implemented on an FPGA, utilizing 70% of available LUTs, 50% of FFs, and 80%of DSPs, with 8.3 Mbits of on-chip memory. The design achieved 100.33 GOP/s throughput, 18 FPS at 55 ms latency, consuming 4.8 W, and delivering an energy efficiency of 20.90 GOP/s/W.

Refka Ghodhbani, Taoufik Saidani, Amani Kachoukh, Mahmoud Salaheldin Elsayed, Yahia Said and Rabie Ahmed, “Hardware-Accelerated Detection of Unauthorized Mining Activities Using YOLOv11 and FPGA” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160494

@article{Ghodhbani2025,
title = {Hardware-Accelerated Detection of Unauthorized Mining Activities Using YOLOv11 and FPGA},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160494},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160494},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Refka Ghodhbani and Taoufik Saidani and Amani Kachoukh and Mahmoud Salaheldin Elsayed and Yahia Said and Rabie Ahmed}
}