# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 5 Conclusions  
This study aims to enhance target detection under foggy conditions by proposing a novel model, AODs-CLYOLO, which integrates dehazing and detection capabilities. In the data preprocessing phase, we introduce a new dehazing model, AODs, which not only improves the dehazing effect but also enhances detection accuracy. During the detection phase, AODs-CLYOLO introduces a new cross-stage local network, CSPCM++, as a key component of the detection head. To address the issue of poor detection performance on large targets, AODs-CLYOLO incorporates the LSKNet selective attention mechanism before the SPP layer and at the output of the large target detection layer. Moreover, considering the application scenarios of target detection in foggy conditions, we have upgraded the model to be more lightweight. We replaced the convolutional layers in the neck with depth-wise separable convolutions, reducing the model's parameter count while improving detection metrics. Experimental results show that compared to the baseline model YOLOv5s, our model achieves an increase of 3.7% in [EMAIL_ADDRESS] and 10.1% in [EMAIL_ADDRESS]-0.95 on the synthetic foggy dataset VOC_ fog. These results demonstrate that the improved model exhibits superior performance in target detection under adverse weather conditions such as haze and low visibility. This advancement is particularly significant for fields like autonomous driving and surveillance systems, as it enhances the robustness and accuracy of the systems in challenging weather conditions. Although our model has achieved improvements in target detection performance, there remains room for further research. For instance, we have not yet explored joint training of the dehazing and target detection models. Future work will focus on exploring more complex model structures, a more diverse range of datasets, and more refined model optimization and training strategies in the context of joint training to improve the model's robustness and generalization capabilities. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**AODs**} & \multirow{2}{*}{**CSPCM++**} & \multirow{2}{*}{**LSKNet**} & \multirow{2}{*}{**FocalGIoU**} & \multirow{2}{*}{**DWConv**} & \multicolumn{2}{c}{**mAP\%**} \\ \cline{5-8}  & & & & & & **0.5** & **0.5–0.95** \\ \hline A & - & - & - & - & - & 80.80 & 54.30 \\ B & \(\surd\) & - & - & - & - & 80.90 & 55.10 \\ C & \(\surd\) & \(\surd\) & - & - & - & 82.80 & 58.10 \\ D & \(\surd\) & - & \(\surd\) & - & - & 83.10 & 56.90 \\ E & \(\surd\) & - & - & \(\surd\) & - & 81.30 & 55.30 \\ F & \(\surd\) & \(\surd\) & \(\surd\) & - & - & 83.50 & 59.20 \\ G & \(\surd\) & \(\surd\) & \(\surd\) & - & \(\surd\) & 82.90 & 59.00 \\ H & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 83.80 & 59.80 \\ \hline \hline \end{tabular}
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Table 4: Ablation experiments with object detection modules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**R**} & \multicolumn{2}{c}{**mAP\%**} \\ \cline{4-5}  & & & **0.5** & **0.5-0.95** \\ \hline YOLOv5s & 85.50 & 73.20 & 80.80 & 54.30 \\ ConvMixer & 88.60 & 72.10 & 81.40 & 57.10 \\ ConvMixer++ & 87.20 & 74.60 & 82.22 & 57.70 \\ CSPCM & 88.10 & 73.60 & 82.80 & 58.10 \\ CSPCM++ & 88.90 & 74.00 & 83.30 & 58.80 \\ \hline \hline \end{tabular}
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Table 5: Convmixer, Convmixer++, CSPCM, and CSPCM++ experimental results. **Author Contributions:** Conceptualization, X.L. and Z.L. ; methodology, L.L. ; software, J.C.; validation, Z.L, X.L. and L.L. ; formal analysis, X.L. ; investigation, J.C.; resources, L.L. ; data curation, X.L. ; writing--original draft preparation, X.L. ; writing--review and editing, X.L. ; visualization, X.L. ; supervision, J.C.; project administration, L.L. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.