# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 1 Introduction  
In recent years, convolutional neural networks (CNNs) have been actively investigated in computer vision fields for object detection [1; 2; 3], semantic segmentation [1], and super-resolution [4]. Among the computer vision tasks, object detection, which is a fundamental task, is mainly used in autonomous driving, CCTV, crack detection [5; 6], and industrial fields.  
Deep learning object detection methods are two-stage or one-stage methods; in detail, models can be anchor-based or anchor-free. The two-step method performs localization and classification based on region-of-interest (RoI) selection using a region proposal network that searches for regions in an image that is likely to contain the desired object. Region-based CNN (R-CNN) [7; 8; 9] is a representative two-stage method. The one-stage method performs localization and classification simultaneously, providing favorable detection accuracy. However, it is less accurate than the two-stage method. Representative one-stage methods include SSD [10] and YOLO series [11; 12]. In addition, the one-stage method is improved by extending the network depth by adding a feature pyramid [13] that utilizes feature maps at different scales. However, this method has a class imbalance problem. Therefore, RetinaNet appears, which solves this by assigning different weights to easyand hard samples. Another method, FCOS [14], improves the class imbalance problem due to space constraints, such as anchor-free methods, that do not predefine anchors. Recently, object detection has been studied for lightweight networks to be applied to various industrial environments. As a representative method, two networks, DualNet [15], which utilizes high and low-resolution features, and DPNet [16; 17], a lightweight network that utilizes different resolutions and feature convergence, have been proposed. As such, object detection is a method of high accuracy with low computational cost.  
In this paper, we propose an anchor-free method based on FCOS, called dual-path lightweight module FCOS, which achieves an optimal trade-off between computational cost and accuracy. DLMFCOS comprises a DLM, FPN, and lightweight detection head. First, the proposed DLM minimized feature loss by calculating the spatial and channel information in parallel to improve detection accuracy. Second, bottom-up and top-down structures are added to the conventional FPN to minimize the low-level feature loss of the baseline FCOS. Finally, the lightweight detection head maintains accuracy and minimizes computation. The proposed method was compared with the baseline FCOS in the public datasets PASCAL VOC and MS COCO datasets. The result is an average 1.5% improvement in detection accuracy at a 2.4M lower computational cost. Furthermore, the ablation study usefulness between the proposed methods was verified. The main contributions of this study are as follows:  
* The proposed DLM optimal trade-off computational cost and accuracy compared to standard convolution by minimizing spatial and channel information loss.
* The proposed feature pyramid structure achieves a balance between accuracy and computational cost by improving the feature loss compared to the conventional methods.
* The proposed method minimizes computational costs by improving the conventional detection head structure.