# 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. CNN; EfficientNet; feature pyramid; lightweight deep learning; object detection +
Footnote †: journal:  
hand, MobileNet is a method to perform convolution in units of channels (depthwise) and feature points (pointwise) instead of employing the existing convolution method. These methods can save the computation cost for about 8 times than that of the standard convolution. Alternatively, there is a network weight compression method, where the filter weights use the floating-point format and 32 bits from a memory point of view. The over-parametrization in the general deep learning networks exists that may lead to overfitting. Therefore, a quantization method that reduces the number of bits to a specific number can be applied, resulting in a loss because the weight expression range is reduced and reducing the storage capacity of the network. CNN-based object detection methods are categorized into two-stage and one-stage detector methods. The two-stage detector, which includes the mask region-based CNN (RCNN) [10], performs classification after searching for a region proposal in which an object may exist. Although this method has high localization and object accuracy, it is not suitable for real-time detection because it consumes a lot of search time when searching for proposals. The one-stage detector, such as such as YOLOv3 [11] and SSD300 [12], performs classification and localization of objects through one network propagation, which uses a predefined anchor box (prior box). This method is an improvement of the two-stage detector method. Based on the aforementioned information, various lightweight techniques should be used for efficient object detection. For the existing detection networks, it is important to minimize the trade-off between accuracy and inference speed. Therefore, this study proposes a new object detection network--efficient shot detector (ESDet) using a lightweight deep learning method. The main contributions of this paper are:  
1. A lightweight pyramid-structured object detection network with few parameters is proposed. Although it uses fewer channels than the existing pyramid structure, it is possible to efficiently extract features with a structure that repeats the number of times. In addition, it is designed to suppress unnecessary feature information by adding a feature refining process in the pyramid structure. 2. The one-stage detection method uses a prior box because it detects each feature map grid. In this paper, we redesigned the prior box to be robust to small and large objects. 3.