Abstract
Traffic signs play an important role in warning drivers by providing information about traffic restrictions, directions, and road quality in order to make sure that every driver is kept safe. Over the past decade, the development of autonomous vehicles has been an active area of research. Therefore, automatic traffic sign detection is a crucial part of the intelligent transportation systems that can be used in autonomous vehicles to detect traffic signs on the road. The optimization methods based on a biologically inspired computation are very powerful in solving optimization problems. In this work, we consider the traffic sign detection task as an optimization problem and propose the artificial bee colony (ABC) method, one of the most popular biologically inspired methods, as an alternative approach for solving it. In other words, we aim to present an algorithm for the automatic detection of multiple traffic signs with a circular shape based on solutions generated by the ABC method without considering the conventional Hough transform principles. Experimental results obtained by our method demonstrate that the proposed approach works well for multiple traffic sign detection and outperforms other existing algorithms.
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References
Abdi L, Meddeb A (2017) Deep learning traffic sign detection, recognition and augmentation. In: Proceedings of the 32nd ACM SIGAPP Symposium on Applied Computing (SAC 2017), pp 131–136
Banharnsakun A (2017) Hybrid ABC-ANN for pavement surface distress detection and classification. Int J Mach Learn Cybernet 8(2):699–710
Banharnsakun A, Achalakul T, Batra RC (2012) Target finding and obstacle avoidance algorithm for microrobot swarms. In: International Conference on Systems, Man, and Cybernetics (SMC 2012), pp 1610–1615
Berkaya SK, Gunduz H, Ozsen O, Akinlar C, Gunal S (2016) On circular traffic sign detection and recognition. Expert Syst Appl 48:67–75
Celik T (2010) Change detection in satellite images using a genetic algorithm approach. IEEE Geosci Remote Sens Lett 7(2):386–390
Cuevas E, Sención-Echauri F, Zaldivar D, Pérez-Cisneros M (2012) Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput 16(2):281–296
Escalera S, Baró X, Pujol O, Vitrià J, Radeva P (2011) Background on traffic sign detection and recognition. In: Traffic-Sign Recognition Systems. Springer, London, pp 5–13
Flanders H, Price JJ (2014) Calculus with analytic geometry. Academic Press, London
García-Garrido M, Sotelo M, Martín-Gorostiza E (2005) Fast road sign detection using Hough transform for assisted driving of road vehicles. In: International Conference on Computer Aided Systems Theory (EUROCAST 2005), pp. 543–548
González D, Pérez J, Milanés V, Nashashibi F (2016) A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst 17(4):1135–1145
Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2017) Multiple thresholding and subspace based approach for detection and recognition of traffic sign. Multimed Tools Appl 76(5):6973–6991
Kang F, Li JJ, Xu Q (2012) Damage detection based on improved particle swarm optimization using vibration data. Appl Soft Comput 12(8):2329–2335
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey
Karaboga D, Akay B (2009) A comparative study of Artificial Bee Colony algorithm. Appl Math Comput 214(1):108–132
Karaboga D, Basturk B (2008) On the performance of Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Khan JF, Bhuiyan SM, Adhami RR (2011) Image segmentation and shape analysis for road-sign detection. IEEE Trans Intell Transp Syst 12(1):83–96
Kiran CG, Prabhu LV, Rajeev K (2009) Traffic sign detection and pattern recognition using support vector machine. In: 7th International Conference on Advances in Pattern Recognition (ICAPR 2009), pp 87–90
Kobayashi M, Baba M, Ohtani K, Li L (2015) A method for traffic sign detection and recognition based on genetic algorithm. In: IEEE/SICE International Symposium on System Integration (SII 2015), pp 455–460
Kuo WJ, Lin CC (2007) Two-stage road sign detection and recognition. In: International Conference on Multimedia and Expo (ICME 2007), pp 1427–1430
Lee YJ, Song JB (2010) Autonomous salient feature detection through salient cues in an HSV color space for visual indoor simultaneous localization and mapping. Adv Robot 24(11):1595–1613
Li Y, Zhang M, Liu Y, Xiong Z (2012) Fish-eye distortion correction based on midpoint circle algorithm. In: International Conference on Systems, Man, and Cybernetics (SMC 2012), pp 2224–2228
Liu H, Liu D, Xin J (2002) Real-time recognition of road traffic sign in motion image based on genetic algorithm. In: International Conference Machine Learning and Cybernetics (ICMLC 2002), vol. 1, pp 83–86
Mogelmose A, Trivedi MM, Moeslund TB (2012) Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Transp Syst 13(4):1484–1497
Mohan BC, Baskaran R (2012) A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst Appl 39(4):4618–4627
Mussi L, Cagnoni S, Daolio F (2009) GPU-based road sign detection using particle swarm optimization. In: 9th International Conference on Intelligent Systems Design and Applications (ISDA ‘09), pp 152–157
O’Flaherty C (ed) (1997) Transport planning and traffic engineering. Elsevier
Prasartvit T, Banharnsakun A, Kaewkamnerdpong B, Achalakul T (2013) Reducing bioinformatics data dimension with ABC-kNN. Neurocomputing 116:367–381
Sebastian P, Voon YV, Comley R (2008) The effect of colour space on tracking robustness. In: 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), pp 2512–2516
Surinwarangkoon T, Nitsuwat S, Moore EJ (2012) Traffic sign recognition by color filtering and particle swarm optimization. In: 4th International Conference on Computer Research and Development (IPCSIT), vol. 39
Tan KS, Isa NAM (2011) Color image segmentation using histogram thresholding–Fuzzy C-means hybrid approach. Pattern Recogn 44(1):1–15
Wali SB, Hannan MA, Abdullah S, Hussain A, Samad SA (2015) Shape matching and color segmentation based traffic sign detection system. Przeglad Elektrotechniczny, pp 36–40
Wu Y, Liu Y, Li J, Liu H, Hu X (2013) Traffic sign detection based on convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN 2013), pp 1–7
Zhang H, Luo D (2006) A PSO-based method for traffic stop-sign detection. In: 6th World Congress on Intelligent Control and Automation (WCICA 2006), vol 2, pp 8625–8629
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Banharnsakun, A. Multiple traffic sign detection based on the artificial bee colony method. Evolving Systems 9, 255–264 (2018). https://doi.org/10.1007/s12530-017-9215-7
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DOI: https://doi.org/10.1007/s12530-017-9215-7