Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Mohammad Junaid, Zsolt Szalay and Árpád Török
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Mohammad Junaid: Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Sztoczek Str. 6, J. Building, V. Floor, 1111 Budapest, Hungary
Zsolt Szalay: Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Sztoczek Str. 6, J. Building, V. Floor, 1111 Budapest, Hungary
Árpád Török: Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Sztoczek Str. 6, J. Building, V. Floor, 1111 Budapest, Hungary

Energies, 2021, vol. 14, issue 21, 1-16

Abstract: Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

Keywords: Mask R-CNN; transfer learning; inverse gamma correction; illumination; instance segmentation; pedestrian custom dataset (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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