Abstract
Object detection is an area of computer vision with applications in several contexts such as biomedicine and security; and it is currently growing thanks to the availability of datasets of images, and the use of deep learning techniques. In order to apply object detection algorithms is instrumental to know the quality of the regions detected by them; however, such an evaluation is usually performed using ad-hoc tools for each concrete problem; and, up to the best of our knowledge, it does not exist a simple and generic tool to conduct this task. In this paper, we present DetectionEvaluationJ an open-source tool that has been designed to evaluate the goodness of object detection algorithms in any context and using several metrics. This tool is independent from the programming language employed to implement the detection algorithms and also from the concrete problem where such algorithms are applied.
Partially supported by Ministerio de Industria, Economía y Competitividad, project MTM2014-54151-P.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alonso, A., et al.: AntibiogramJ: a tool for analysing images from disk diffusion tests. Comput. Methods Program. Biomed. 143, 159–169 (2017)
Codella, N., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4), 5:1–5:15 (2017)
Everingham, M., et al.: The PASCAL visual object classes challenge 2012 (VOC2012) results (2012). http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Evjen, B., et al.: Professional XML. Wiley Publishing Inc., Hoboken (2007)
Ghasemian, F., et al.: An efficient method for automatic morphological abnormality detection from human sperm images. Comput. Methods Program. Biomed. 122(3), 409–420 (2015)
Glas, A.S., et al.: The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56, 1129–1135 (2003)
Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016. arXiv preprint arXiv:1605.01397 (2016)
Heras, J., et al.: GelJ - a tool for analyzing DNA fingerprint gel images. BMC Bioinf. 16, 270 (2015)
Heras, J., et al.: Surveying and benchmarking techniques to analyse DNA gel fingerprint images. Brief. Bioinf. 17(6), 912–925 (2015)
Huang, S.C., Chen, B.H.: Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems. IEEE Trans. Neural Netw. Learn. Syst. 24(12), 1920–1931 (2013)
Kaehler, A., Bradski, G.: Learning OpenCV 3. O’Reilly Media, Sebastopol (2015)
Lasko, T.A., et al.: The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inf. 38(5), 404–415 (2005)
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Mata, G., et al.: SynapCountJ: a validated tool for analyzing synaptic densities in neurons. Commun. Comput. Inf. Sci. 690, 41–55 (2017)
MathWorks: Matlab version 9.0.0.341360 (R2016a). The MathWorks Inc., Natick, Massachusetts (2016)
Orlando, J.I., et al.: A discriminatively trained fully connected conditional random field model for blood blessed segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16–27 (2015)
Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2(1), 37–63 (2011)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint arXiv:1612.08242 (2016)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)
Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9(7), 671–675 (2012)
Shaoqing, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Silva, J.S., et al.: Algorithm versus physicians variability evaluation in the cardiac chambers extraction. IEEE Trans. Inf. Technol. Biomed. 16(5), 835–841 (2012)
Wolf, C., Jolion, J.M.: Object count/area graphs for the evaluation of object detection and segmentation algorithms. Int. J. Doc. Anal. Recogn. 8, 280–296 (2006)
Zalama, E., et al.: Road crack detection using visual features extracted by gabor filters. Comput. Aided Civil Infrastruct. Eng. 29, 342–358 (2014)
Zhai, M., et al.: Object detection in surveillance video from dense trajectories. In: 14th IAPR International Conference on Machine Vision Applications. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Domínguez, C., García, M., Heras, J., Inés, A., Mata, E., Pascual, V. (2018). DetectionEvaluationJ: A Tool to Evaluate Object Detection Algorithms. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-74727-9_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74726-2
Online ISBN: 978-3-319-74727-9
eBook Packages: Computer ScienceComputer Science (R0)