Impact of Image Data Splitting on the Performance of Automotive Perception Systems
Paper i proceeding, 2024

Context: 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. Objective: 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. Methods: 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. Results: The mean average precision (mAP) was 0.943 and 0.841 when the similarity-based and the frame-based splitting techniques were applied, respectively. However, the object-based splitting technique produces the worst mAP score (0.118). Conclusion: There are significant differences in the performance of object detection methods when applying different data-splitting techniques. The most positive results are the random selections, whereas the most objective ones are splits based on sequences that represent different geographical locations.

Object detection

Autonomous driving

Image perception system

Data splitting technique



Md Abu Ahammed Babu


Software Engineering 1

Sushant Kumar Pandey

Göteborgs universitet

Darko Durisic


Ashok Chaitanya Koppisetty


Miroslaw Staron

Göteborgs universitet

Lecture Notes in Business Information Processing

1865-1348 (ISSN) 18651356 (eISSN)

Vol. 505 LNBIP 91-111
9783031562808 (ISBN)

16th International Conference on Software Quality, SWQD 2024
Vienna, Austria,





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