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.








Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Piao J, Mcdonald M (2008) Advanced driver assistance systems from autonomous to cooperative approach. Transp Rev 28(5):684–695
Mita T, Kaneko T, Hori O (2005) Joint haar-like features for face detection. Tenth IEEE Int Conf Computer Vision 2:1619–1626
Ma X, Grimson WEL (2005) Edge-based rich representation for vehicle classification. Tenth IEEE Int Conf Computer Vision 2:1185–1192
Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425
Lan R, Lu H, Zhou Y et al (2019) An lbp encoding scheme jointly using quaternionic representation and angular information. Neural Comp Appl 1–7
Kazemi FM , Samadi S , Pourreza HR et al (2007) Vehicle recognition using curvelet transform and svm. Int Conf Inform Technol 516–521
Freund Y, Schapire RE (1997) A desicion-theoretic generalization of on-line learning and an application to boosting. Comput Syst 55(1):119–139
Lan R, Zhou Y, Liu Z et al (2018) Prior knowledge-based probabilistic collaborative representation for visual recognition. IEEE Trans Cybernet 50(4):1498–1508
Chen, Shuhan et al (2018) Reverse Attention for Salient Object Detection. Eur Conf Computer Vision 236–52
Liu W, Anguelov D, Erhan D et al (2016) SSD: Single shot multibox detector. European conference on computer vision 21–37
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Int Conf Machine Learn 448–456
Ekrem B, Altun Y (2017) Classification of vehicles in traffic and detection faulty vehicles by using ann techniques. Electric Electron Computer Sci. https://doi.org/10.1109/EBBT.2017.7956758
Druzhkov PN, Kustikova VD (2016) A survey of deep learning methods and software tools for image classification and object detection. Pattern Recogn Image Analy 26(1):9–15
Zhou X, Wei G, Fu WL, Du F (2017) Application of deep learning in object detection. Int Conf Computer Inform Sci 631–634
Serikawa S, Hui L (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50
Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Computer Vision Pattern Recogn 580–587
Girshick R. (2015) Fast r-cnn. Int Conf Computer Vision 1440–1448
Ren S, He K, Girshick R et al (2015) Faster r-cnn : towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Machine Intell 39(6):1137–1149
Redmon J, Divvala S, Girshick R et al (2016) You only look once: unified, real-time object detection. Computer Vision Pattern Recogn 779–788
Karen S, Andrew Z (2015) Very deep convolutional networks for large-scale image recognition. Int Conf Learn Represent 1–14
Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. Computer Vis Pattern Recogn 6517–6525
Thorson IL, Liénard J, David SV (2015) The essential complexity of auditory receptive fields. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1004628
Ning N C, Zhou N H, Song N Y et al (2017) Inception single shot multibox detector for object detection. IEEE Int Conf Multimedia Expo Workshops 549–554
Hartigan JA, Wong MA (1979) Algorithm as 136: A k-means clustering algorithm. J Royal Statist Soc 28(1):100–108
Geiger A, Lenz P, Stiller C et al (2013) Vision meets robotics: The kitti dataset. Int J Robot Res 32(11):1231–1237
Ranjeeth KC, Anuradha R (2020) Feature selection and classification methods for vehicle tracking and detection. J Amb Intell Human Comput. https://doi.org/10.1007/s12652-020-01824-3
Armengol E (2019) Constructing a classifier with patterns. J Amb Intell HumanComput. https://doi.org/10.1007/s12652-019-01514-9
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02085-w