# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 4. Results
### Performance of Claylo  
The proposed AODs-CLYOLO model was trained and validated using dehazed datasets, which we refer to as VOC_train_dehaze and VOC_test_dehaze. To ensure the fairness and reliability of the results, all models were trained and tested in the same experimental environment. All comparison models were trained under the same hardware and software conditions, following the same training configurations to ensure comparability of the results and trained for 300 epochs. Specifically, the comparison models YOLOv7-tiny, YOLOv4, YOLOv3, Faster R-CNN, and SSD were trained with a batch size of 16, and other settings remained consistent with those in the original papers. Additionally, the training datasets for the comparison models all used VOC_train with randomly added haze, and the validation datasets used VOC_test. From Table 2, we can see that our model's Mean Average Precision ([EMAIL_ADDRESS]) improves by 3.75 percentage points compared to the baseline model YOLOv5s across all categories. For applications requiring high-precision detection in autonomous driving scenarios, [EMAIL_ADDRESS] provides a stringent evaluation of the model's fine-grained detection capabilities, reflecting its actual performance in these contexts. As shown in the table, our model achieves a [EMAIL_ADDRESS] of 59.80, which is 10.13 percentage points higher than the baseline. Additionally, our model also shows improvements in Precision and Recall, reaching 88.00 and 74.70, respectively. Overall, the enhanced AODs-CLYOLO model outperforms the compared algorithms in all metrics. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **mAP** & **mAP** & \multicolumn{5}{c}{**AP\% 0.5**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**R**} \\  & **@0.5** & **@0.95** & & & & & & \\ \hline Faster-RCNN & 76.87 & 44.90 & 76.85 & 70.53 & 75.05 & 84.20 & 77.73 & 52.87 & 79.20 \\ SSD & 78.78 & 51.50 & 77.80 & 73.17 & 84.78 & 80.53 & 77.62 & 88.53 & 69.85 \\ YOLOv3 & 82.12 & 59.10 & 81.70 & 80.60 & 86.00 & 79.20 & 83.10 & 89.40 & 74.30 \\ YOLOv4 & 81.80 & 53.90 & 86.34 & 76.44 & 80.33 & 86.51 & 85.01 & 90.25 & 72.32 \\ YOLOv5n & 78.10 & 50.50 & 80.00 & 81.30 & 77.30 & 73.90 & 78.10 & 84.30 & 68.80 \\ YOLOv7-tiny & 83.10 & 56.20 & 83.60 & 85.30 & 83.10 & 79.60 & 83.80 & 84.40 & 76.30 \\ IA-YOLO [22] & 72.03 & - & - & - & - & - & - & - & - \\ YOLOv5s & 80.80 & 54.30 & 82.50 & 83.90 & 81.10 & 77.50 & 78.70 & 85.50 & 73.20 \\ Ours & 83.80 & 59.50 & 83.10 & 85.60 & 83.80 & 80.50 & 83.80 & 88.00 & 74.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experiments on the object detection part of the evaluation on the VOC_test dataset. To comprehensively evaluate the computational performance of our proposed model, we also compared several mainstream object detection algorithms under the same experimental environment. Table 3 summarizes the performance of different models in terms of the number of parameters, floating point operations per second (FLOPs), and memory usage. Through the comparison of these metrics, we can clearly demonstrate the differences in computational efficiency and resource consumption among the models. The data in the table show that our model outperforms models like Faster R-CNN, SSD, YOLOv3, and YOLOv4 in terms of inference time and memory usage, but it falls short compared to some lightweight models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny. However, our model manages to maintain a controlled increase in parameters while demonstrating significant advantages in object detection accuracy (as seen in the experimental results in Table 2). These results indicate that, in the trade-off between computational performance and detection accuracy, our model offers unique value in tasks where high detection precision is crucial. To more intuitively demonstrate the superior detection performance, we compared the object detection results with prediction boxes of our algorithm against YOLOv5s.