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A novel Single-shot Multi-scale detection network with feature fusion and multi-scale attention mechanism is proposed for autonomous driving. The proposed model is referred to as a Single-shot Multi-scale Attentive Detector (SSAD) and it would build feature relations of the feature map in the spatial space. The proposed network highlights pedestrian and vehicle regions on the extracted feature map and also suppresses irrelevant regions, from the global relation information and thereby providing reliable guidance for autonomous driving, while detecting smaller and occluded targets. SSAD design is simple, accurate and computationally efficient. Evaluation results of the proposed network on multiple datasets have achieved considerable and promising results. Experimental results show that the SSAD network when tested on Pascal Voc-2007, INRIA, Caltech, and City Persons datasets, outperforms many state-of-the-art (SOTA) detectors.

SSAD: Single-Shot Multi-Scale Attentive Detector for Autonomous Driving