  A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion approach based on the dynamic analysis of agreement among
object detection outputs is proposed. Furthermore, we propose an online versus
just in training image augmentation strategy. Experiments comparing the results
both with and without fusion are presented. We demonstrate that the augmented
and fused combination results are the best, with respect to higher accuracy
rates and reduction of outlier influences. The approach is demonstrated in the
context of cone, pedestrian and box detection for Advanced Driver Assistance
Systems (ADAS) applications.
