The relation between object area and accuracy for a modern mobile convolutional object detector
M.S.C. Rijlaarsdam (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Object detectors, much like humans, perform less well on small than on large objects. Because of this, the object size distribution of a dataset influences the average precision a network achieves on that dataset. Therefore, the object size/precision curve of a network might be a better way to compare convolutional object detectors than the average precision over an entire dataset. In this paper we measure the relationship between object size and accuracy
for a modern mobile convolutional object detector. We verify that this relationship holds for a different dataset, and that the dataset object size distribution influences the average precision over the entire dataset. We conclude that the object size/accuracy curve might contain more information about a network’s performance than the average precision over an entire dataset.