The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (5): 68-81.doi: 10.19682/j.cnki.1005-8885.2021.0023

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Exploring the usefulness of light field super-resolution for object detection


  • Received:2020-09-17 Revised:2020-12-22 Online:2021-10-31 Published:2021-10-29
  • Contact: fan .shi
  • Supported by:
    National Natural Science Foundation of China;National Key R&D Program of China


In order to solve the impact of image degradation on object detection, an object detection method based on light field super-resolution ( LFSR) is proposed. This method takes LFSR as an image enhancement step to provide high- quality images for object detection without using expensive imaging equipment. To evaluate this method, three types of objects: person, bicycle, and car, are chosen and the results are compared from 5 parts: detected object quantity, mean confidence score, detection results in different scenes, error detection, and detection results from different images sizes and detection speed. Experimental results based on the common object in context ( COCO) dataset show that the method incorporated LFSR improves performance of object detection models.

Key words:

light-field ( LF), super-resolution ( SR), object detection, RetinaNet, YOLOv3, TinyYOLOv3