Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

  1. Chaves, Deisy
  2. Saikia, Surajit
  3. Fernández-Robles, Laura
  4. Alegre, Enrique
  5. Trujillo, Maria
Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2018

Volume: 15

Issue: 3

Pages: 231-242

Type: Article

DOI: 10.4995/RIAI.2018.10229 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

Currently, many applications require a precise localization of the objects that appear in an image, to later process them. This is the case of visual inspection in the industry, computer-aided clinical diagnostic systems, the obstacle detection in vehicles or in robots, among others. However, several factors such as the quality of the image and the appearance of the objects to be detected make this automatic location difficult. In this article, we carry out a systematic revision of the main methods used to locate objects by considering since the methods based on sliding windows, as the detector proposed by Viola and Jones, until the current methods that use deep learning networks, such as Faster-RCNN or Mask-RCNN. For each proposal, we describe the relevant details, considering their advantages and disadvantages, as well as the main applications of these methods in various areas. This paper aims to provide a clean and condensed review of the state of the art of these techniques, their usefulness and their implementations in order to facilitate their knowledge and use by any researcher that requires locating objects in digital images. We conclude this work by summarizing the main ideas presented and discussing the future trends of these methods.

Bibliographic References

  • Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E., 2016. A region based convolutional network for tumor detection and classification in breast mammography. In: Deep Learning and Data Labe-ling for Medical Applications. pp. 197–205.
  • Alexe, B., Deselaers, T., Ferrari, V., 2010. What is an object? In: CVPR. pp.73–80.
  • Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., Zuair, M., 2017.Deep learning approach for car detection in uav imagery. Remote Sens. 9 (4). DOI:10.3390/rs9040312
  • Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A training algorithm for opti-mal margin classifiers. In: COLT. pp. 144–152.
  • Brazil, G., Yin, X., Liu, X., 2017. Illuminating pedestrians via simultaneous detection & segmentation. CoRR abs/1706.08564.
  • Cai, Z., Fan, Q., Feris, R. S., Vasconcelos, N., 2016. A unified multi-scale deep convolutional neural network for fast object detection. CoRRabs/1607.07155.
  • Cao, X., Gong, G., Liu, M.,Qi, J., 2016. Foreign object debris detection on air-field pavement using region based convolution neural network. In: DICTA. pp. 1–6. DOI:10.1109/DICTA.2016.7797045
  • Cao, X., Wang, P., Meng, C., Bai, X., Gong, G., Liu, M., Qi, J., 2018. Region based cnn for foreign object debris detection on airfield pavement. Sensors18 (3). DOI:10.3390/s18030737
  • Chen, J., Liu, Z., Wang, H., Núñez, A., Han, Z., 2018. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE T Instrum Meas 67 (2), 257–269. DOI:10.1109/TIM.2017.2775345
  • Cireʂan, D. C., Giusti, A., Gambardella, L. M., Schmidhuber, J., 2013. Mitosis detection in breast cancer histology images with deep neural networks. In: MICCAI. pp. 411–418.
  • Coifman, B., McCord, M., Mishalani, R. G., Iswalt, M., Ji, Y., 2006. Roadway traffic monitoring from an unmanned aerial vehicle. IEE Proceedings - Intelligent Transport Systems 153 (1),11–20. DOI:10.1049/ip-its:20055014
  • Dai, J., Li, Y., He, K., Sun, J., 2016. R-FCN: object detection via region-based fully convolutional networks. CoRR abs/1605.06409.
  • Dalal, N., Triggs, B., June2005. Histograms of oriented gradients for human detection. In: CVPR. Vol. 1. pp. 886–893 vol. 1. DOI:10.1109/CVPR.2005.177
  • Deng, L., 2014. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing 3, e2.
  • Deng, L., Yu, D., 2014. Deep learning: Methods and applications. Foundations and Trends in Signal Processing 7 (3-4), 197–387.
  • Dollár, P., Tu, Z., Perona, P., Belongie, S. J., 2009. Integral channel features. In: BMVC. pp. 1–11.
  • Dollar, P., Zitnick, L., 2013. Structured forests for fast edge detection. In: ICCV. pp. 1841–1848.
  • Donoser, M., Bischof, H., 2006. Efficient maximally stable extremal region (mser) tracking. In: CVPR. pp. 553–560. DOI:10.1109/CVPR.2006.107
  • Du, X., El-Khamy, M., Lee, J., Davis, L., 2017. Fused dnn: A deep neural net-work fusion approach to fast and robust pedestrian detection. In: WACV. pp.953–961. DOI:10.1109/WACV.2017.111
  • Dženan, Z., Aleš, V., Jan, E., Daniel, H., Christopher, N., Andreas, K., 2014. Robust detection and segmentation for diagnosis of vertebral diseases using routine mr images. Computer Graphics Forum 33 (6), 190–204. DOI:10.1111/cgf.12343
  • Felzenszwalb, P. F., Girshick, R. B., McAllester, D., Ramanan, D., 2010. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32 (9), 1627–1645. DOI:10.1109/TPAMI.2009.167
  • Felzenszwalb, P. F., Huttenlocher, D. P., 2004. Efficient graph-based image segmentation. IJCV 59 (2), 167–181. DOI:10.1023/B:VISI.0000022288.19776.77
  • Ferguson, M., Ak, R., Lee, Y. T. T., Law, K. H., 2017. Automatic localization of casting defects with convolutional neural networks. In: IEEE International Conference on Big Data. pp. 1726–1735. DOI:10.1109/BigData.2017.8258115
  • Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N., 2017a. Machine-vision-based identification of broken inserts in edge profile milling heads. Robot Comput Integr Manuf 44, 276 – 283. DOI:https://doi.org/10.1016/j.rcim.2016.10.004
  • Fernández-Robles, L., Azzopardi, G., Alegre, E., Petkov, N., Castejón-Limas ,M., 2017b. Identification of milling inserts in situ based on a versatile machine vision system. JMSY 45, 48 – 57. DOI: https://doi.org/10.1016/j.jmsy.2017.08.002
  • Freund, Y., Schapire, R. E., 1999. A short introduction to boosting. In: IJCAI. pp. 1401–1406.
  • García-Ordás, M. T., Alegre, E., González-Castro, V., Alaiz-Rodríguez, R.,2017. A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int J Adv Manuf Technol 90 (5), 1947–1961. DOI:10.1007/s00170-016-9541-0
  • García-Olalla, O., Alegre, E., Fernández-Robles, L., Fidalgo, E., Saikia, S., 2018. Textile retrieval based on image content from cdc and webcam cameras in indoor environments. Sensors 18 (5). DOI:10.3390/s18051329
  • Garnett, N., Silberstein, S., Oron, S., Fetaya, E., Verner, U., Ayash, A., Goldner,V., Cohen, R., Horn, K., Levi, D., 2017. Real-time category-based and general obstacle detection for autonomous driving. In: ICCVW. pp. 198–205. DOI:10.1109/ICCVW.2017.32
  • Girshick, R. B., 2015. Fast R-CNN. CoRR abs/1504.08083.
  • Girshick, R. B., Donahue, J., Darrell, T., Malik, J., 2013. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRRabs/1311.2524.
  • He, B., Xiao, D., Hu, Q., Jia, F., 2018. Automatic magnetic resonance image prostate segmentation based on adaptive feature learning probability boos-ting tree initialization and cnn-asm refinement. IEEE Access 6, 2005–2015.
  • He, K., Gkioxari, G., Doll ́ar, P., Girshick, R. B., 2017. Mask R-CNN. CoRRabs/1703.06870.
  • He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: CVPR. pp. 770–778.
  • Heo, Y. J., Lee, D., Kang, J., Lee, K., Chung, W. K., 2017. Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip. Scientific Reports 7 (1), 11651. DOI:10.1038/s41598-017-11534-0
  • Hosang, J., Benenson, R., Doll ́ar, P., Schiele, B., 2016. What makes for effective detection proposals? IEEE Trans. Pattern Anal. Mach. Intell. 38 (4),814–830. DOI:10.1109/TPAMI.2015.2465908
  • Jiamin, L., David, W., Le, L., Zhuoshi, W., Lauren, K., B., T. E., Berkman,S., A., P. N., M., S. R., 2017. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks. Medical Physics44 (9), 4630–4642. DOI:10.1002/mp.12399
  • Jung, F., Kirschner, M., Wesarg, S., 2013. A generic approach to organ detection using 3d haar-like features. In: Bildverarbeitung für die Medizin 2013.pp. 320–325.
  • Kisilev, P., Sason, E., Barkan, E., Hashoul, S., 2016. Medical image description nusing multi-task-loss cnn. In: Deep Learning and Data Labeling for Medical Applications. pp. 121–129.
  • Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In: Adv Neural Inf Process Syst. pp. 1097–1105.
  • Lampert, C. H., Blaschko, M. B., Hofmann, T., 2008. Beyond sliding windows: Object localization by efficient subwindow search. In: CVPR. pp. 1–8. DOI:10.1109/CVPR.2008.4587586
  • Lecun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436–444.
  • Lee, C. J., Tseng, T. H., Huang, B. J., Jun-Weihsieh, Tsai, C. M., 2015. Obstacle detection and avoidance via cascade classifier for wheeled mobile robot. In: ICMLC. Vol. 1. pp. 403–407. DOI:10.1109/ICMLC.2015.7340955
  • Lee, J., Wang, J., Crandall, D., Šabanovic, S., Fox, G., 2017. Real-time, cloud-based object detection for unmanned aerial vehicles. In: IRC. pp. 36–43. DOI:10.1109/IRC.2017.77
  • Levi, D., Garnett, N., Fetaya, E., September 2015a. Stixelnet: A deep convolutional network for obstacle detection and road segmentation. In: BMVC. pp. 109.1–109.12. DOI:10.5244/C.29.109
  • Levi, D., Garnett, N., Fetaya, E., 2015b. Stixelnet: A deep convolutional network for obstacle detection and road segmentation. In: BMVC. pp. 109.1–109.12. DOI:10.5244/C.29.109
  • Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S., 2018. Scale-aware fast r-cnn for pedestrian detection. IEEE Trans Multimedia 20 (4), 985–996. DOI:10.1109/TMM.2017.2759508
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.,2016. Ssd: Single shot multibox detector. In: ECCV. pp. 21–37.
  • Luo, S., Lu, H., Xiao, J., Yu, Q., Zheng, Z., 2017. Robot detection and localization based on deep learning. In: CAC. pp. 7091–7095.
  • Ma, Y., Jiang, Z., Zhang, H., Xie, F., Zheng, Y., Shi, H., 2017. Proposing regions from histopathological whole slide image for retrieval using selective search. In: ISBI. pp. 156–159. DOI:10.1109/ISBI.2017.7950491
  • Mery, D., Rio, V., Zscherpel, U., Mondrag ́on, G., Lillo, I., Zuccar, I., Lobel,H., Carrasco, M., 2015. Gdxray: The database of x-ray images for nondestructive testing. Journal of Nondestructive Evaluation 34 (4), 42. DOI:10.1007/s10921-015-0315-7
  • Park, J.-K., Kwon, B.-K., Park, J.-H., Kang, D.-J., 2016. Machine learning-based imaging system for surface defect inspection. IJPEM-GT 3 (3), 303–310. DOI:10.1007/s40684-016-0039-x
  • Redmon, J., Divvala, S. K., Girshick, R. B., Farhadi, A., 2015. You only look once: Unified, real-time object detection. CoRR abs/1506.02640.
  • Ren, S., He, K., Girshick, R. B., Sun, J., 2015. Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497.
  • Říha, K., Mašek, J., Burget, R., Beneš, R., Závodná, E., 2013. Novel method for localization of common carotid artery transverse section in ultrasound images using modified viola-jones detector. Ultrasound Med Biol 39 (10),1887 – 1902. DOI:10.1016/j.ultrasmedbio.2013.04.013
  • Sa, R., Owens, W., Wiegand, R., Studin, M., Capoferri, D., Barooha, K.,Greaux, A., Rattray, R., Hutton, A., Cintineo, J., Chaudhary, V., 2017. Intervertebral disc detection in x-ray images using faster r-cnn. In: EMBC. pp. 564–567. DOI:10.1109/EMBC.2017.8036887
  • Saikia, S., Fidalgo, E., Alegre, E., Fernández-Robles, L., 2017. Object detection for crime scene evidence analysis using deep learning. In: ICIAP. pp.14–24.
  • Sepúlveda, G. V., Torriti, M. T.,Calero, M. F., 2017. Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado. Revista Iberoamericana de Automática e Informática Industrial14 (2), 152–162. DOI:10.1016/j.riai.2016.09.010
  • Shah, V. R., Maru, S. V., Jhaveri, R. H., 2018. An obstacle detection scheme for vehicles in an intelligent transportation system. IJCNIS 8 (10), 23–28. DOI:10.5815/ijcnis.2016.10.03
  • Shi, Y., Li, Y., Wei, X., Zhou, Y., 2017. A faster-rcnn based chemical fiber paper tube defect detection method. In: International Conference on Enterprise Systems. pp. 173–177. DOI:10.1109/ES.2017.35
  • Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556.
  • Szegedy, C., Ioe, S., Vanhoucke, V., Alemi, A. A., 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI. pp. 4278–4284.
  • Tang, T., Zhou, S., Deng, Z., Zou, H., Lei, L., 2017. Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors 17 (2). DOI:10.3390/s17020336
  • Tek, F., 2013. Mitosis detection using generic features and an ensemble of cascade adaboosts. J Pathol Inform 4 (1), 12. DOI:10.4103/2153-3539.112697
  • Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., Smeulders, A. W. M. ,2013. Selective search for object recognition. IJCV 104 (2), 154–171.
  • Viola, P., Jones, M. J., May 2004. Robust real-time face detection. IJCV 57 (2), 137–154 .DOI:10.1023/B:VISI.0000013087.49260.fb
  • Wang, S., Cheng, J., Liu, H., Tang, M., 2018. Pcn: Part and context information for pedestrian detection with cnns. CoRR abs/1804.04483.
  • Xu, Y., Yu, G., Wang, Y., Ma, Y., 2017a. Car detection from low-altitude uav imagery with the faster r-cnn. JAT 2017. DOI:https://doi.org/10.1155/2017/2823617
  • Xu, Y., Yu, G., Wang, Y., Wu, X., Ma, Y., 2016. A hybrid vehicle detection method based on viola-jones and hog+svm from uav images. Sensors 16 (8). DOI:10.3390/s16081325
  • Xu, Y., Yu, G., Wu, X., Wang, Y., Ma, Y., 2017b. An enhanced viola-jones vehicle detection method from unmanned aerial vehicles imagery. IEEE trans Intell Transp Syst 18 (7), 1845–1856. DOI:10.1109/TITS.2016.2617202
  • Yang, S., Fang, B., Tang, W., Wu, X., Qian, J., Yang, W., 2017. Faster r-cnn based microscopic cell detection. In: SPAC. pp. 345–350. DOI:10.1109/SPAC.2017.8304302
  • Yi, X., Song, G., Derong, T., Dong, G., Liang, S., Yuqiong, W., 2018. Fast road obstacle detection method based on maximally stable extremal regions. IJARS 15 (1), 1–10. DOI:10.1177/1729881418759118
  • Zeiler, M. D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: ECCV. pp. 818–833.
  • Zhang, L., Lin, L., Liang, X., He, K., 2016. Is faster r-cnn doing well for pedestrian detection? In: ECCV. pp. 443–457.
  • Zhong, J., Lei, T., Yao, G., 2017. Robust vehicle detection in aerial images based on cascaded convolutional neural networks. Sensors 17 (12). DOI:10.3390/s17122720
  • Zitnick, L., Dollar, P., 2014. Edge boxes: Locating object proposals from edges. In: ECCV. pp. 391–405.