Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid

Abstract

Convolutional-neural-network (CNN)-based methods are continuously used in various industries with the rapid development of deep learning technologies. However, an inference efficiency problem was reported in applications that require real-time performance, such as a mobile device. It is important to design a lightweight network that can be used in general-purpose environments such as mobile environments and GPU environments. In this study, we propose a lightweight network efficient shot detector (ESDet) based on deep training with small parameters. The feature extraction process was performed using depthwise and pointwise convolution to minimize the computational complexity of the proposed network. The subsequent layer was formed in a feature pyramid structure to ensure that the extracted features were robust to multiscale objects. The network was trained by defining a prior box optimized for the data set of each feature scale. We defined an ESDet-baseline with optimal parameters through experiments and expanded it by gradually increasing the input resolution for detection accuracy. ESDet training and evaluation was performed using the PASCAL VOC and MS COCO2017 Dataset. Moreover, the average precision (AP) evaluation index was used for quantitative evaluation of detection performance. Finally, superior detection efficiency was demonstrated through the experiment compared to the conventional detection method.

The Prediction of Incremental Damage on Optics from the Final Optic Assembly in an ICF High-Power Laser Facility

“…The deep learning model adopted a feature pyramid network structure based on ResNet-18, which had 18 main layers, with 16 convolutional layers and 2 fully connected layers, as illustrated in Figure 12. In this study, transfer learning and the feature pyramid network [41,42] were employed to pinpoint the real damage of elements. The deep learning model was pretrained using a public dataset containing similar scenes, while the last several layers of the pretrained model were fine-tuned using online-offline image sample data of elements after data augmentation.…”