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In this paper, we introduce an innovative method to improve the convergencespeed and accuracy of object detection neural networks. Our approach,CONVERGE-FAST-AUXNET, is based on employing multiple, dependent loss metricsand weighting them optimally using an on-line trained auxiliary network.Experiments are performed in the well-known RoboCup@Work challenge environment.A fully convolutional segmentation network is trained on detecting objects'pickup points. We empirically obtain an approximate measure for the rate ofsuccess of a robotic pickup operation based on the accuracy of the objectdetection network. Our experiments show that adding an optimally weightedEuclidean distance loss to a network trained on the commonly used Intersectionover Union (IoU) metric reduces the convergence time by 42.48%. The estimatedpickup rate is improved by 39.90%. Compared to state-of-the-art task weightingmethods, the improvement is 24.5% in convergence, and 15.8% on the estimatedpickup rate.

Fast Convergence for Object Detection by Learning how to Combine Error Functions