Image segmentation is the most important step for any visual scene understanding system. In this paper, we use a semantic approach where each pixel is labeled with a semantic object category. Location of objects inside a tunnel’s road is a crucial task for an automatic tunnel incident detection system. It needs in particular to accurately detect and localize different types of zones, such as road lane, emergency lane, and sidewalk. Unfortunately, the existing methods often fail in providing acceptable image regions due to dynamic environment conditions: change in the lighting conditions, shadow appearance, objects variability, etc. To overcome these difficulties, we proposed to use the semantic tunnel image segmentation approach and a Convolutional Neural Network (CNN) to solve this problem. To evaluate the performance of the proposed approach, we performed a comparison to the state of the art and recent methods on two different datasets collected from two tunnels in France, called the ”T1” and ”T2”. Our extensive study leads to the provide of the best tunnel scene segmentation approach. The proposed method has been deployed by VINCI Autoroutes company in a real-world environment for automatic incident detection system.
Automatic License Plate Recognition (ALPR) is a technology designed to automatically read vehicle license plates. Traditional ALPR systems first detect the License Plate (LP), then apply the Optical Character Recognition (OCR) pipeline, which includes LP image pre-processing, character segmentation, character classification and post-processing. An ALPR system developed with such approaches often fails to provide acceptable results due to numerous challenging situations, which significantly increase the appearance variability of LPs as well as the characters to be classified. Recently, Convolutional Neural Network (CNN) models have proved efficient for ALPR problems. However, many of these CNN-based methods yet exhibit vulnerabilities to properly localize the region of the characters’ sequence and therefore provide an incorrect segmentation. Herein, this paper presents a novel real-time ALPR system that uses the concept of saliency map within the CNN model. The key contribution is at the segmentation step where the characters are located by means of the saliency map, which helps to refine the character classification step. The proposed ALPR pipeline consists of the two modules: 1) LPlocalization-CNN to detect the LP and 2) Saliency-Map-CNN to segment the characters in the LP. Experiments are conducted on a private and two public datasets and the proposed method is compared to the state-of-the-art methods. Results show that it performs well with respect to both accuracy and computation time, and hence clearly demonstrate the usefulness of the proposed system for the real-world ALPR applications.