Philippe Foucher

Detection and recognition of urban road markings using images

While road lane markings detection was extensively studied, in particular for intelligent vehicle applications, the detection and recognition of all kind of marking such as arrows, crosswalks, zebras, words, pictograms, continuous and discontinuous lane markings was drastically less studied. However, it has many potential applications in the design of advanced driver assistance systems, as well as for asset management along itineraries. An algorithm is proposed which is based on the following processing steps: marking pixel extraction, detection using connected components before Inverse Perspective Mapping and recognition based on the comparison with a single pattern or with repetitive rectangular patterns. The proposed algorithm is able to detect and recognize repetitive markings (such as crosswalks) as well as single patterns (such as arrows). We believe that the proposed algorithm can be extended easily to solve the problem of the identification of all types of markings.

Road Sign Detection in Images: A Case Study

Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the same ground-truth image database, results obtained by three algorithms that sample different state-of-the-art approaches. The three tested algorithms: Contour Fitting, Radial Symmetry Transform, and pair-wise voting scheme, all use color and edge information and are based on geometrical models of road signs. The test dataset is made of 847 images 960x1080 of complex urban scenes (available at www.itowns.fr/benchmarking.html). They feature 251 road signs of different shapes (circular, rectangular, triangular), sizes and types. The pros and cons of the three algorithms are discussed, allowing to draw new research perspectives.

Robust road marking extraction in urban environments using stereo images

Most road marking detection systems use image processing to extract potential marking elements in their first stage. Hence, the performances of extraction algorithms clearly impact the result of the whole process. In this paper, we address the problem of extracting road markings in high resolution environment images taken by inspection vehicles in a urban context. This situation is challenging since large special markings, such as crosswalks, zebras or pictographs must be detected as well as lane markings. Moreover, urban images feature many white elements that might lure the extraction process. In prior work an efficient extraction process, called Median Local Threshold algorithm, was proposed that can handle all kinds of road markings. This extraction algorithm is here improved and compared to other extraction algorithms. An experimental study performed on a database of images with ground-truth shows that the stereovision strategy reduces the number of false alarms without significant loss of true detection.

Adjustment of Sonar and Laser Acquisition Data for Building the 3D Reference Model of a Canal Tunnel

In this paper, we focus on the construction of a full 3D model of a canal tunnel by combining terrestrial laser (for its above-water part) and sonar (for its underwater part) scans collected from static acquisitions. The modeling of such a structure is challenging because the sonar device is used in a narrow environment that induces many artifacts. Moreover, the location and the orientation of the sonar device are unknown. In our approach, sonar data are first simultaneously denoised and meshed. Then, above- and under-water point clouds are co-registered to generate directly the full 3D model of the canal tunnel. Faced with the lack of overlap between both models, we introduce a robust algorithm that relies on geometrical entities and partially-immersed targets, which are visible in both the laser and sonar point clouds. A full 3D model, visually promising, of the entrance of a canal tunnel is obtained. The analysis of the method raises several improvement directions that will help with obtaining more accurate models, in a more automated way, in the limits of the involved technology.

An Image-Based Inspection System for Canal Tunnel Heritage

Nowadays, 33 canal tunnels, mainly bored during the 19th and 20th centuries, are still used in France for commercial navigation and boating. The preservation of this heritage is not only essential for cultural and historical reasons but also for economical necessities and security issues. A French partnership proposes to develop a prototype dedicated to the image-based inspection of canal tunnels. In this paper, we address the problems of image quality and location accuracy, which is particularly challenging since no conventional global referencing system is available in tunnels. We introduce the dynamic imaging system that has been tested during recording campaigns in Niderviller tunnel. Several solutions to locate the inspection barge have been also investigated. The experimental evaluation on the accuracy of the referencing methods is presented and analyzed.

Route Segmentation into Speed Limit Categories by using Image Analysis

In this contribution, we address the problem of road sequence segmentation into speed limit categories, as perceived by the user. We propose an algorithm that is based on two processing steps. First, the images are classified independently using a standard random forest algorithm. Low-level and high-level approaches are proposed and compared. In the second phase, a sequential smoothing of the results using different filters is applied. An evaluation based on two databases of images with ground truth shows the pros and cons of the methods.

Evaluating a Static Multibeam Sonar Scanner for 3D Surveys in Confined Underwater Environments

Mechanical Sonar Scanning (MSS) is a recent technology that allows sonar to be used for static measurements in the same way as Terrestrial Laser Scanners (TLS), which makes it an attractive tool for underwater infrastructure surveys. Nevertheless, the metrological capabilities of this type of device have been little explored in the literature, particularly in narrow and shallow environments. In this paper, we report on the experimental assessment of a recent MSS, the BlueView BV5000, in a lock. The 3D sonar scans performed with the system suspended from the surface are registered using an innovative algorithm that exploits external measurements from a total station and the symmetry of the structure. We review the different errors that impair sonar data, and compare the resulting point cloud to a TLS model that was acquired the day before, while the lock was completely emptied for maintenance. After correcting a tilt angle calibration error, the maximum difference is less than 10 cm, and the standard deviation is about 3 cm. Visual inspection shows that coarse defects of the masonry, such as stone lacks or cavities, can be detected in the MSS point cloud, while details smaller than 4 cm, e.g., damaged joints, are harder to notice.