Abstract
In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.
Highlights
Pedestrian detection refers to a research problem in which a pedestrian is judged in a specific scene and a specific position of the pedestrian is given
In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments
The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant
Summary
Pedestrian detection refers to a research problem in which a pedestrian is judged in a specific scene and a specific position of the pedestrian is given. For the problems of pedestrian detection under low light, the main work of this paper is as follows: 1) For the problem of difficult feature extraction in low-light environment, sample pre-processing is performed before the Faster R-CNN model training by multi-scale Retinex image enhancement method; 2) For the problem of inaccurate positioning of the bounding box, the Soft-NMS algorithm is more effective in improving the detection accuracy; 3) In order to verify the performance of the proposed algorithm, the low-light pedestrian image dataset with annotations is trained under the algorithm to evaluate the performance of the proposed algorithm. For dealing with relatively small pedestrians in low light, the resolution of the feature map extracted by the ROI pooling layer in the Faster R-CNN algorithm will be relatively low, so this paper takes a multi-scale Retinex image enhancement method on the data set to preprocess the sample to improve the resolution, thereby improving the accuracy of pedestrian detection.
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