Improving Object Localization with Fitness NMS and Bounded IoU Loss

We demonstrate that many detection methods are designed to identify only a sufficently accurate bounding box, rather than the best available one. To address this issue we propose a simple and fast modification to the existing methods called Fitness NMS. This method is tested with the DeNet model and obtains a significantly improved MAP at greater localization accuracies without a loss in evaluation rate. Next we derive a novel bounding box regression loss based on a set of IoU upper bounds that better matches the goal of IoU maximization while still providing good convergence properties. Following these novelties we investigate RoI clustering schemes for improving evaluation rates for the DeNet \textit{wide} model variants and provide an analysis of localization performance at various input image dimensions. We obtain a MAP[0.5:0.95] of 33.6\%@79Hz and 41.8\%@5Hz for MSCOCO and a Titan X (Maxwell).