New Metric for Evaluation of Deep Neural Network Applied in Vision-Based Systems

Abstract

Vision-based object detection plays a crucial role for the complete functionality of many engineering systems. Typically, detectors or classifiers are used to detect objects or to distinguish different targets. This contribution presents a new evaluation of CNN classifiers in image detection using a modified Probability of Detection reliability measure. The proposed method allows the evaluation of further image parameters affecting the classification results. The proposed evaluation method is implemented on images and comparisons made on parameters with the best detection capability. A typical certification standard (90/95) denoting a 90% probability of detection at 95% reliability level is adapted and successfully applied. Using the 90/95 standard, comparisons are made between different image parameters. A noise analysis procedure is introduced, permitting the trade-off between the detection rate, false alarms, and process parameters. The advantage of the novel approach is experimentally evaluated for vision-based classification results of CNN considering different image parameters. With this new POD evaluation, classifiers will become a trustworthy part of vision systems.