Abstract
With the recent development of artificial intelligence along with information and communications infrastructure, a new paradigm of online services is being developed. Whereas in the past a service system could only exchange information of the service provider at the request of the user, information can now be provided by automatically analyzing a particular need, even without a direct user request. This also holds for online platforms of used-vehicle sales. In the past, consumers needed to inconveniently determine and classify the quality of information through static data provided by service and information providers. As a result, this service field has been harmful to consumers owing to such problems as false sales, fraud, and exaggerated advertising. Despite significant efforts of platform providers, there are limited human resources for censoring the vast amounts of data uploaded by sellers. Therefore, in this study, an algorithm called YOLOv3+MSSIM Type 2 for automatically censoring the data of used-vehicle sales on an online platform was developed. To this end, an artificial intelligence system that can automatically analyze an object in a vehicle video uploaded by a seller, and an artificial intelligence system that can filter the vehicle-specific terms and profanity from the seller’s video presentation, were also developed. As a result of evaluating the developed system, the average execution speed of the proposed YOLOv3+MSSIM Type 2 algorithm was 78.6 ms faster than that of the pure YOLOv3 algorithm, and the average frame rate per second was improved by 40.22 fps. In addition, the average GPU utilization rate was improved by 23.05%, proving the efficiency.
Highlights
With the recent and rapid advancements in deep learning and hardware performance, object detection, which can be used to obtain information by automatically finding and classifying objects in an image, has become available in the field of image processing [1,2,3,4,5,6]
The system implemented in this study presents the system target performance indicators for commercialization services and an evaluation of the target achievement was requested to the Korea
The success or failure of the stable commercialization service of the system developed in this study depends on the performance of the video process system. This is because the YOLOv3 object recognition algorithm used in the video process system accounts for a large number of computing resources of an artificial intelligence server, and a significant amount of time is spent on the video analysis
Summary
With the recent and rapid advancements in deep learning and hardware performance, object detection, which can be used to obtain information by automatically finding and classifying objects in an image, has become available in the field of image processing [1,2,3,4,5,6] Such advances in object recognition technology have been implemented in artificial intelligence services, such as with robots, autonomous vehicles, and autonomous drones, enabling such machines to determine the current situation and take action autonomously without human intervention. It is expected that the response speed and service experience of the proposed platform can be improved by increasing the object recognition speed without changing the network of the object recognition algorithm
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