Abstract
An adaptive confidence algorithm for vehicle detection is proposed. Shortcomings of traditional vehicle detection method of deep learning with fixed confidence threshold are analyzed. To deal with false alarms and false negatives brought by these shortcomings, an adaptive confidence threshold algorithm for vehicle detection is raised. Two datasets are set up to test and compare the detection performance of the adaptive confidence algorithm with fixed confidence on MS-CNN. The experimental results show that the proposed algorithm could effectively reduce the false alarm rate and improve the detection performance of MS-CNN. It is of certain significance for the application of deep learning technology to vehicle detection.
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