# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 4 Procedure and Results
### Experimental Results  
In this section, we evaluate our approach by comparing it with one multispectral model and two single-spectral models. In order to evaluate the performance of our proposed multispectral recognition method, we also trained three other object detectors, including two faster RCNN models trained by color or thermal images only and a vanilla ConvNet. To facilitate the distinction, we named the faster RCNN trained by visible images as F-rgb and the faster RCNN trained by thermal images as F-ther. The intersection-over-union (IOU), non-maximum suppression (nms), weight decay, momentum and other parameters of reference models are consistent with our model. Figure 5 shows the curve of the miss rate and false positives per image under all-weather conditions of the four models, where the miss rate is the number of objects not recognized by the detector divided by the total number of ground truth and the false positives (FPs) are those negative samples identified by the detector as positive samples. It can be seen from the figure that neither single spectral recognizer is as effective as the fused recognizer. Their miss rate and FPs per image are high in most cases, and the comparison of the two fused recognizers shows that our method is an improvement over vanilla ConvNet. The reason why we use vanilla ConvNet as a comparison here is that it performs better in the fusion detector, especially for pedestrian recognition [27]. During the testing process, we quantify the performance of our approach by computing the mean average precision (mAP). In particular, we evaluate the D-F Net on six different weather conditions (sunny day, clear night, rainy day, rainy night, reflection and blur) to simulate the working environment. The calculation results are shown in Table 2, it can be seen from the data that the present approach has improved performance in different environments, the mAP of our approach is 75.05% for good weather and 65.15% for bad weather, while the mAP of vanilla ConvNet is 67.9% for good weather and 50.95% for bad weather. According to the results, the accuracy of the present method is improved by 7.15% for good weather and 14.2% for bad weather compared with vanilla ConvNet. Figure 6a compares the training process of loss function between the vanilla ConvNet and our D FRCNN. It can be seen that in the training process, the D-F Net model can converge rapidly, and the loss function is smaller than the vanilla ConvNet after about 30,000 iterations, in which the vanilla ConvNet shows signs of overfitting. Figure 6b is the comparison of receiver operating characteristic curve (ROC) between our approach and the vanilla ConvNet. We know that the area enclosed by the ROC curve and coordinate axis is an index to measure the performance of the model. The larger the area, the better the recognition performance of the detector. We can see from Figure 6b that our proposed method has more reliable performance and better robustness than vanilla ConvNet under the premise of considering complex weather conditions, because it is more accurate under the same recall rate. Figure 7 shows the detection results for some bad weather conditions, these images were selected from the test set, including sunny, rainy, snowy and blurry lens conditions. As we can see from these test results, our method can still ensure the accuracy of recognition under some special circumstances by adopting multispectral information fusion. In addition, we adopt the layers of base network 2 conv3_3, conv4_3 and base network 1 conv5_3 to extract features, and the detector is also able to recognize targets with a large size difference (such as the pedestrian and the bus in the picture on the top left).