Week 7 (05/4 – 11/4) – Occupancy grid conversion to image, edge detection and study of the influence of cell resolution. Quantitative evaluation of the method

This week started with the conversion of the density grid to an OpenCV image. This was a big addition because it opened a lot of new doors in edge detection filters like canny and LoG.

OpenCV images are very easily manipulated and can be converted back to occupancy grids with thresholds to eliminate low-density zones that do not correspond to road limits.

Video 1 shows the result of multiple edge detection filters as well as the study of the cell resolution influence in the density grid. After multiple tests, the grid resolution was then changed from 0.2m/cell to 0.4m/cell. Video 2 shows the obtained results from this revision.

Vid. 1. Cell resolution study and OpenCV edge detection filters.
Vid. 2. Resolution to 0.4m/cell.

With the need to evaluate the quality of the limits identified a literature review was conducted where that the most common way to evaluate the performance of a road limits detection algorithm found is by calculating the four statistical quantifiers presented in figure 1, where the obtained results are matched to a ground-truth of about 500-100 frames. For each algorithm, these indicators should be calculated in different situations, for example, in a straight and curved road, in the presence of positive and negative road curbs.

Since there are no databases for this kind of work, road segments are to be selected and road limits manually identified to create a database for each described situation to later calculate the indicators with the number of TP (true positives), FP (false positives), TN (true negatives) and FN (false negatives).

Fig. 1. Statistical measures. (2.1) Precision (Positive predictive value). (2.2) Specificity (True negative rate). (2.3) Negative predictive value. (2.4) Sensitivity (Total positive rate).

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