Abstract
CenterNet is a widely used single-stage anchor-free object detector. It only uses single feature map to detect all size objects, and does not effectively use different levels of feature maps. We present an enhanced feature fusion and multi receptive field object detector, named EM-CenterNet. Our detector first fuses different levels of feature maps, and then enhances feature fusion through semantic information transfer path. Besides, we design another key component, which is composed of continuous several dilated convolutions and shortcut connections, so that our detector can cover all object’s scales. We compare the EM-CenterNet method with the baseline on the Pascal VOC and COCO datasets. Experiments show that our method increases the AP by 12.2% on the Pascal VOC dataset, and increases the AP by 5.9% on the COCO dataset.
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Liu, H., Cui, J., Zhong, H., Huang, C. (2022). Enhanced Feature Fusion and Multiple Receptive Fields Object Detection. In: Yang, S., Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2022. Communications in Computer and Information Science, vol 1700. Springer, Singapore. https://doi.org/10.1007/978-981-19-7946-0_11
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