# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 4 Experiment Results and Discussion
### Comparison with Other Networks
#### 4.2.1 PASCAL VOC07  
Table 3 shows the accuracies and numbers of computations of the compared methods on the PASCAL VOC07 test dataset. The proposed method achieves high accuracy with low computation overhead compared to the two-step method. Compared with the standard FCOS detector, our improved model showed a 1.7% accuracy improvement with 7.5% fewer computations. There was also a 10% improvement in inference time.  
Compared with other one-stage backbone networks (e.g., VGG-16, ResNet, and Dark-Net), our ResNet-50 method achieves high accuracy with only a small amount of computation. Moreover, the spatial and channel feature losses are minimized by the DLM, which enables more efficient feature extraction. By strengthening the lateral connections between feature maps with the proposed FPN structure, low-level information was supplemented. Thus, we can confirm that the detection performance and inferencing speed of the proposed method clearly improved, resulting in a good balance between the amount of computation and accuracy.  
Figure 9 compares the detection results of the baseline FCOS detector, and the proposed method using the PASCAL VOC07 test dataset, which includes occluded object detection. The proposed method demonstrated better detection results than the baseline FCOS detector because it overcame the low-level information loss, which enabled it to detect occluded objects better, as illustrated in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detector** & **Backbone** & **Input Resolution** & **Parmas (M)** & **FLOPs (G)** & **mAP (\%)** & **FPS** \\ \hline \multicolumn{6}{c}{Two-stage} \\ \hline Fast R-CNN [7] & VGG-16 [31] & \(\sim\)1000 \(\times\) 600 & - & - & 70.0 & 0.5 \\ Faster R-CNN [8] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 42.0 & - & 73.2 & 7 \\ OHEM [32] & VGG-16 & \(\sim\)1000 \(\times\) 600 & - & - & 74.6 & 7 \\ HyperNet [33] & VGG-16 & \(\sim\)1000 \(\times\) 600 & - & - & 76.3 & 0.88 \\ Fast R-CNN & ResNet-101 & \(\sim\)1000 \(\times\) 600 & 60.0 & - & 76.4 & 2.4 \\ ION [34] & VGG-16 & \(\sim\)1000 \(\times\) 600 & - & - & 76.5 & 1.25 \\ R-FCN [35] & ResNet-101 & \(\sim\)1000 \(\times\) 600 & 50.9 & - & 80.5 & 9 \\ CoupleNet [36] & Resnet-101 & \(\sim\)800 \(\times\) 1024 & - & - & 82.7 & 8.2 \\ \hline \multicolumn{6}{c}{One-stage} \\ \hline YOLO & GoogLeNet & 448 \(\times\) 448 & - & - & 63.4 & 45 \\ RON & VGG-16 & 384 \(\times\) 384 & - & - & 75.4 & 15 \\ SSD321 & ResNet-101 & 321 \(\times\) 321 & - & 77.1 & 11.2 & \\ SSD300 [10] & VGG-16 & 300 \(\times\) 300 & 26.3 & - & 77.2 & 46 \\ FCOS & ResNet-50 & 512 \(\times\) 512 & 32.1 & 103.1 & 78.3 & 58 \\ YOLOv2 & DarkNet-19 & 544 \(\times\) 544 & 51.0 & - & 78.6 & 40 \\ DSSD321 [37] & ResNet-101 & 321 \(\times\) 321 & - & - & 78.6 & 9.5 \\ SSD512 & VGG-16 & 512 \(\times\) 512 & 29.4 & - & 79.8 & 19 \\ SSD513 & ResNet-101 & 513 \(\times\) 513 & - & - & 80.6 & 6.8 \\ DSSD513 & ResNet-101 & 513 \(\times\) 513 & - & - & 81.5 & 5.5 \\ \hline Proposed method & ResNet-50 & 512 \(\times\) 512 & 29.7 & 68.2 & 80.0 & 62 \\ \hline \hline \end