Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles

06/06/2022

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by   Hulin Li, et al.

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Object detection is a difficult downstream task in computer vision. For the on-board edge computing platforms, a giant model is difficult to achieve the real-time detection requirement. And, a lightweight model built from a large number of the depth-wise separable convolutional layers cannot achieve the sufficient accuracy. We introduce a new method, GSConv, to lighten the model but maintain the accuracy. The GSConv balances the model's accuracy and speed better. And, we provide a design paradigm, slim-neck, to achieve a higher computational cost-effectiveness of the detectors. In experiments, our method obtains state-of-the-art results (e.g. 70.9 of  100FPS on a Tesla T4) compared with the original networks. Code will be open source.