Abstract
The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.
Highlights
The image intensity gradients were found with the Canny edge detector, and the image intensity gradients were found by applying nonmaximum suppression in order to dispose of the edge detection spurious response
We evaluated deep learning methods for detecting objects in poor visibility conditions for video recorders
The images were processed using the Canny operator, which allowed for the disposal of the damaged image blocks
Summary
The trend in the development of modern urbanism is the transition to digital technologies to improve the efficiency of managing complex distributed objects in urban environments. The main goal is to achieve the sustainable functioning of the systems for ensuring the life of the urban population. Data mining, deep learning, and predictive analytics are of particular importance for the implementation of the Smart Sustainable City concept. The transition to the concept of the sustainability of the urban environment requires the development of proactive intelligent systems that are designed to prevent the risks of the occurrence and development of critical events at the distributed infrastructure facilities of the urban environment, which include the Sustainability 2022, 14, 2420.
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