A Cascaded R-CNN With Multiscale Attention and Imbalanced Samples for Traffic Sign Detection

Abstract

In recent years, the deep learning is applied to the field of traffic sign detection methods which achieves excellent performance. However, there are two main challenges in traffic sign detection to be solve urgently. For one thing, some traffic signs of small size are more difficult to detect than those of large size so that the small traffic signs are undetected. For another, some false signs are always detected because of interferences caused by the illumination variation, bad weather and some signs similar to the true traffic signs. Therefore, to solve the undetection and false detection, we first propose a cascaded R-CNN to obtain the multiscale features in pyramids. Each layer of the cascaded network except the first layer fuses the output bounding box of the previous one layer for joint training. This method contributes to the traffic sign detection. Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve the accuracy of the traffic sign detection. Finally, we increase the number of difficult negative samples for dataset balance and data augmentation in the training to relieve the interference by complex environment and similar false traffic signs. The data augment method expands the German traffic sign training dataset by simulation of complex environment changes.

Existing System

Contrast-limited adaptive histogram equalization (CLAHE)

Proposed System

We propose a multiscale cascaded object detection network and introduce multiscale features in pyramids to obtain feature of each scale. Each estimated position by skip connection of every feature is trained to obtain features of other scales, which relieves the overfitting. Finally, the high level semantic information and low level spatial information extracted from the multiscale object detection network are fused. We propose a multiscale attention method to do dot-product and softmax by multiscale features itself to gain the weights, which is summed to fine the features to highlight the traffic sign features and better detect the traffic signs in complex background. The similar objects are generally confounded with true objects in complex environment. Aimed at the balancing the distribution of traffic sign categories, we increase the number of hard negative samples in the training stage.

Architecture

BLOCK DIAGRAM