DetectionEvaluationJ: A Tool to Evaluate Object Detection Algorithms

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.

[1]  C. L. Philip Chen,et al.  Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery , 2011, Int. J. Mach. Learn. Cybern..

[2]  Jónathan Heras,et al.  SynapCountJ: A Validated Tool for Analyzing Synaptic Densities in Neurons , 2016, BIOSTEC.

[3]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[4]  Seyed Abolghasem Mirroshandel,et al.  An efficient method for automatic morphological abnormality detection from human sperm images , 2015, Comput. Methods Programs Biomed..

[5]  P. Bossuyt,et al.  The diagnostic odds ratio: a single indicator of test performance. , 2003, Journal of clinical epidemiology.

[6]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  César Domínguez,et al.  Antibiogramj: A tool for analysing images from disk diffusion tests , 2017, Comput. Methods Programs Biomed..

[8]  Sharath Pankanti,et al.  Deep learning ensembles for melanoma recognition in dermoscopy images , 2016, IBM J. Res. Dev..

[9]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[10]  Shih-Chia Huang,et al.  Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  Paula Martins,et al.  Algorithm Versus Physicians Variability Evaluation in the Cardiac Chambers Extraction , 2012, IEEE Transactions on Information Technology in Biomedicine.

[13]  César Domínguez,et al.  GelJ – a tool for analyzing DNA fingerprint gel images , 2015, BMC Bioinformatics.

[14]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[15]  César Domínguez,et al.  Surveying and benchmarking techniques to analyse DNA gel fingerprint images , 2015, Briefings Bioinform..

[16]  Jean-Michel Jolion,et al.  Object count/area graphs for the evaluation of object detection and segmentation algorithms , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[17]  Lei Chen,et al.  Object detection in surveillance video from dense trajectories , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[18]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[19]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[20]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.