# A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement
## 4 The Experiments on Different Calibration Method and Using Image Enhancement to Process Samples
### Performance Analysis with Image Enhancement on Pedestrian Datasets  
From the above experiments, we found that the less-category calibration could accurately locate the bounding-box and recognition rate. In this experiment, the fast ACE algorithm is used to automatically enhance the 10,854 pedestrian images and improve the image contrast. The enhancement results are shown in Figures 11 and 12. We use the less-category calibration to calibrate the samples in the dataset and select VGG-16 convolution neural network for training and testing, which are performed on the GPU Tesla K80 based on the deep learning framework of Caffe. There are 20 images in subway tunnels, selected from the testing set which contains 854 images, and we compared the specific detection effect (false and miss) of the single image between the model without enhancement and the model with enhancement, and one of them is shown in Figure 13. As we can see, the false-detection remarked by yellow arrow in Figure 13a does not happen in Figure 13b, and the person with the red arrow in Figure 13a is detected in the Figure 13b. Compared with the datasets without image enhancement, the testing accuracy of enhanced datasets increases by 5.2%. As we can see in the Figure 13, under the condition of dim light, the negative samples are similar to the head-shoulder samples that are easy to attribute to the positive samples for the model without enhancement. Moreover, the model with enhancement has obvious advantages on the sensibility to the color and contour, and less false- and misdetection. Figure 14 shows samples misclassified by the model without image enhancement. Then, twenty images in the subway platform are selected from the testing set. We compared the specific detection effect of the single image between the model without enhancement and the model with enhancement. Due to the high brightness and the large color difference between the background and pedestrian in Figure 15, the difference between the test effects on the two datasets is not obvious. However, compared with the non-enhanced dataset, the test accuracy of enhanced dataset still increases by 2.1%. The person with the red arrow in Figure 15a is detected in the Figure 15b. Figure 14: Samples misclassified by the model without enhancement. Figure 13: The detection effect in subway tunnels. Furthermore, we conduct experiments in the subway tunnel with the number of pedestrians from 2 to 20, and compare the accuracy of the two models. As shown in Figure 16, a horizontal axis is the number of people in the scene; the vertical axis is the difference between correctly recognizing people by two models. The greater the difference is, the better the effect of the model with image enhancement is. The same experiment is also carried out on the subway platform, and the corresponding results are shown in Figure 17. In the bright environment, the difference between the two models is not obvious with the increase of the number of people. The mean average precision and used time taken by the enhanced model and unenhanced model are given in Table 2.  
Figure 16: The relationship between the number of people and difference between correctly recognizing people by two models in subway tunnels. Figure 17: The relationship between the number of people and difference between correctly recognizing people by two models in subway platform. Figure 15: The detection effect in subway platform. In order to further verify that the model with enhancement has obvious advantages on the sensibility to the color and contour of person, 20 images with enhancement and 20 images without enhancement in subway tunnels are selected for testing by the model with enhancement, and one of them is shown in Figure 18. As we can see, two persons with the red arrow in Figure 18a can be detected in the Figure 18b.