Improving the Energy Efficiency of Real-time DNN Object Detection via Compression, Transfer Learning, and Scale Prediction

In recent years, computational accessibility has enabled the use of Deep Neural Network (DNN) for computer vision applications on devices with limited computational resources. We focus on the real-time object detection algorithms deployed on UAV -friendly devices. The hardware deployed on UAV must be lightweight and thus limited in processing power, memory, and storage capacity. Lightweight modeling architecture does not suffice for high-recall reconnaissance applications. In this paper, we propose to reduce power consumption of YOLOv5 DNN architecture. We decided to use compressed convolutional technique, transfer learning, backbone shrinkage, and scale prediction to reduce the number of learnable parameters from the YOLOv5model. Our approach reduced the size of the model significantly and lowered the power consumption in turn. GPU memory and the Billion Floating-Point Operations Per Second (GFLOPS) for the YOLOv5model will keep the performance measure of the model as the baseline state-of-the-art. The best resulting model has a 63.86% mean average precision (mAP) and a GFLOPS of 97.7 on “DIOR”, an overhead imagery data set. The proposed approach has lowered GPU memory consumption of the model by 34% and lowered the energy consumption by 10 Watts compared to the baseline model.