Information Capacity as a Predictor of Perception Performance

The design of automated driving systems is of growing industry and societal interest. Perception is a critical technology for these systems, which allows a vehicle to discern the surrounding environment. Perception systems in automated vehicles frequently use machine vision algorithms; however, the performance of a machine vision algorithm critically depends on the quality of the data provided. Quantifying the ‘quality’ of image data is therefore potentially a useful tool in understanding and predicting the performance of a machine vision system. This study uses the Shannon Information Capacity, a metric based on information theory, to evaluate the impact of image quality on a perception algorithm. In this preliminary study, a set of synthetic objects are arranged to create a novel simulated test chart. The chart contains standard machine vision objects of interest (people and cars) as well as a slanted edge, which is used to calculate image quality metrics. The chart is degraded using varying levels of contrast and blur to simulate different real-world operating conditions. Object detection performance is then evaluated using a range of deep learning-based detection algorithms, with different architectures. The results indicate that Shannon Information Capacity has the potential to predict machine vision performance across multiple model architectures and object types. For example, the results for all the models show that accuracy remains relatively constant above an SIC value of 0.25 b/p. Results indicate that for YOLOv10m SIC has mutual information value with detection accuracy of 1.66 bits while MTF50 has a score of 0.4945 bits. This study is the first to show the correlation between SIC and machine vision performance. While other metrics have been previously shown to have some correlation with machine vision, the correlation shown by SIC is much stronger. The findings presented may be of use to designers of autonomous driving systems and automotive camera manufacturers.