Impact of Image Data Splitting on the Performance of Automotive Perception Systems

Training image recognition systems is one of the crucial elements of the AI Engineering process in general and for automotive systems in particular. The quality of data and the training process can have a profound impact on the quality, performance, and safety of automotive software. Splitting data between train and test sets is one of the crucial elements in this process as it can determine both how well the system learns and generalizes to new data. Typical data splits take into consideration either randomness or timeliness of data points. However, in image recognition systems, the similarity of images is of equal importance. In this computational experiment, we study the impact of six data-splitting techniques. We use an industrial dataset with high-definition color images of driving sequences to train a YOLOv7 network. The results indicate, there are significant differences in the performance of object detection methods when applying different data-splitting techniques. The most positive results are the random selection, whereas the most objective ones are splits based on sequences that represent different geographical locations.

Authors: Md Abu Ahammed Babu, Sushant Kumar Pandey, Darko Durisic, Ashok Chaitanya Koppisetty and Miroslaw Staron

Talk language: English
Level: Expert
Target group:

Company:
Volvo Car Corporation

Md Abu Ahammed Babu