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Real-time scene understanding has become crucial in many applications such asautonomous driving. In this paper, we propose a deep architecture, calledBlitzNet, that jointly performs object detection and semantic segmentation inone forward pass, allowing real-time computations. Besides the computationalgain of having a single network to perform several tasks, we show that objectdetection and semantic segmentation benefit from each other in terms ofaccuracy. Experimental results for VOC and COCO datasets show state-of-the-artperformance for object detection and segmentation among real time systems.

BlitzNet: A Real-Time Deep Network for Scene Understanding