Optimization of Object Detection CNN With Weight Quantization and Scale Factor Consolidation

Due to the current Covid-19 pandemic, Convolutional Neural Networks (CNN) models attract attention in the applications to identify people with no masks. Developing an optimal CNN model is a challenging task especially for embedded deceives with limited hardware resources. To overcome the above challenge, we present a weight quantization technique aimed to produce compact CNN model for detection of people with mask or no mask. Its weights and feature maps are optimized using minimal fixed-point quantization at little or no sacrifice of its detection accuracy. The proposed weight quantization has been evaluated using a modified tiny-YOLOv2 model with the Mask and no-mask. Furthermore, we modified the internal model architecture to further reduce the model size and inference calculation by optimizing the order of max-pooling layers, consolidating the scale factors of batch normalization into only two pre-calculated parameters, and modifying the leaky ReLU activation function. The evaluation demonstrated that it saves more than 50 % of parameter memory and 56.21 % of inference computation.