Edge detective weights initialization on Darknet-19 model for YOLOv2-based facemask detection

Abstract

The object detection model based on the transfer learning approach comprises feature extraction and detection layers. YOLOv2 is among the fastest detection algorithms, which can utilize various pretrained classifier networks for feature extraction. However, reducing the number of network layers and increasing the mean average precision (mAP) together have challenges. Darknet-19-based YOLOv2 model achieved an mAP of 76.78% by having a smaller number of layers than other existing models. This work proposes modification by adding layers that help enhance feature extraction for further increasing the mAP of the model. Above that, the initial weights of the new layers can be random or deterministic, fine-tuned during training. In our work, we introduce a block of layers initialized with deterministic weights derived from several edge detection filter weights. Integrating such a block to the darknet-19-based object detection model improves the mAP to 85.94%, outperforming the other existing model in terms of mAP and number of layers.