YOLOv8 Based Multi Object Detection for BTS Asset Monitoring

The security of Base Transceiver Stations (BTS) is a critical priority due to the significant material losses caused by theft of components such as cables and receivers. Traditional human-based monitoring systems are impractical for 24-hour surveillance due to high costs and logistical challenges. Object detection using deep learning algorithms, specifically the YOLO architecture, potentially offers an effective solution for automated monitoring. YOLO is known for its real-time processing capabilities and was studied for its application in detecting sectoral and microwave antennas on BTS from image data collected by remote camera. In this research, the performance of YOLOv8 in identifying BTS components was investigated through comprehensive experimentation. The result show that this model is quite effective in detecting microwave and sectoral antennas, this shows by 61% overall accuracy.