# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 5 Conclusions  
In this study, we proposed a DLMFCOS object detection network that achieves an optimal balance between accuracy and network size for deep-learning object detection. Conventional methods can provide accuracy (two-stage) or speed (one-stage), but normally not both. We propose to overcome this disparity by improving the one-stage FCOS model so that it minimizes the ordinarily high feature loss caused by channel reduction by processing spatial and channel information in parallel with a novel FPN that contains both bottom-up and top-down structures. The DLM comprises an LSM that efficiently extracts spatial \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Parmas (M)** & **FLOPs (G)** & **mAP (\%)** \\ \hline FCOS + FPN [13] & 32.2 & 103.14 & 78.5 \\ FCOS + PAN [44] & 33.3 & 104.66 & 79.7 \\ FCOS + Proposed & 29.8 & 83.44 & 80.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation study of the proposed feature pyramid structure. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Detector** & & & & & & **Class** & & & & \\ \hline \multirow{4}{*}{FCOS [14]} & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\  & 80.8 & 86.8 & 81.5 & 72.2 & 63.4 & 84.8 & 88.1 & 91.1 & 58.8 & 80.3 \\  & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\  & 66.0 & 88.6 & 86.1 & 84.8 & 84.2 & 51.0 & 80.8 & 72.1 & 88.9 & 79.3 \\ \hline \multirow{4}{*}{DLMFCOS} & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\  & 84.7 & 88.0 & 84.7 & 70.3 & 66.7 & 86.3 & 89.3 & 90.6 & 60.4 & 83.3 \\ \cline{1-1}  & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\ \cline{1-1}  & 71.4 & 91.0 & 87.1 & 85.9 & 85.3 & 50.7 & 83.8 & 76.0 & 88.5 & 79.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The ablation study for DLMFCOS analysis on the PASCAL VOC 2007 test dataset. information using a dilated depth-wise convolution and a CA, resulting in improved weights for each channel for the GAP. The detection head was expanded, and features were extracted through convolution, maintaining better accuracy than the conventional detection head. Moreover, compared to baseline FCOS, the computational cost was greatly reduced. We compared our models' mAP with contemporary models using the PASCAL VOC07 and MS COCO2017 open datasets, and the results show that our DLM FCOS object detection network improved the average accuracy by at least 1.5%, with approximately 33.85% fewer computations. This method finally achieves a good balance between accuracy and efficiency. We expect that our results will contribute to improved object detection networks. In a future study, it will be necessary to experiment with applying the DLM to other object detection networks. In addition, based on a lightweight backbone network, experiments are planned on devices with limited computational performance, such as embedded devices. Conceptualization, B.H. and S.L. ; data curation, B.H. ; formal analysis, B.H. and S.L. ; investigation, B.H. ; methodology, B.H. and S.L. ; project administration, H.H. ; software, B.H. and S.L. ; supervision, S.L. and H.H. ; validation, B.H. and S.L. ; visualization, B.H. ; writing--original draft preparation, B.H. ; writing--review and editing, S.L. and H.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.