Robust Compression Technique for YOLOv3 on Real-Time Vehicle Detection
Nattanon Krittayanawach, Peerapon Vateekul · 2019
For vehicle detection, YOLOv3 has shown promising accuracy. Since the number of parameters in this network can be more than ten million parameters, it cannot be fit into a commodity camera. In this paper, we propose a compression mechanism designed specifically for YOLOv 3's network by removing unnecessary filters. Since YOLOv3 composes of two network components: backbone and pyramid networks, we propose a robust pruning mechanism to prune filters of each network separately. This can help to avoid over-pruning the network in some part of the model making our model more robust. There are two main pruning criteria investigated: Average Percentage of Zero (APoZ) and Sum Magnitude Weight. The experiment was conducted on UA-DETRAC. The results show that our compression mechanism with APoZ criterion can reduce more than 90% of the network size, while the accuracy is even higher than the full model for about 2%.