Abstract
Vehicle detection plays an effective and important role in traffic safety, which has attracted extensive attention from both academic and industry. Deep learning has made significant breakthroughs in vehicle detection application. The Single Shot Detector (SSD) algorithm, which is one of the object detection algorithms, is used to detect vehicles. However, its main challenge is that the computing complexity and low accuracy. In this paper, an improved vehicle detection algorithm based on SSD is proposed to improve accuracy, especially for small vehicles detection. We add an Inception block to the extra layer in the SSD before the prediction to improve its performance. Then we use a new method that is more suitable for vehicle detection to set the scales and aspect ratios of the default bounding boxes, which benefits position regression and maintains the fast speed. The validity of our algorithm is verified on KITTI and UVD datasets. Compared with SSD, our algorithm achieves a higher mean average precision (mAP), while maintaining a fast speed.
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Chen, W., Qiao, Y. & Li, Y. Inception-SSD: An improved single shot detector for vehicle detection. J Ambient Intell Human Comput 13, 5047–5053 (2022). https://doi.org/10.1007/s12652-020-02085-w
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DOI: https://doi.org/10.1007/s12652-020-02085-w