diff --git a/README.md b/README.md index 36507c50e9899e5f979bffa7ef0bee5f8d8dfd0c..4d35c27cde6dc762807be33b06607f2c1bfdc6b7 100644 --- a/README.md +++ b/README.md @@ -82,6 +82,13 @@ and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments. + + + +Fig. 4. Mapping the detected objects by all sensors in Fig. 2 on (a) radar coordinates and (b) RGB camera image. ’F’ box in the bottom left corner of +each figure indicates the location of the ferry. + + # References 1. F. Farahnakian, M.Haghbayan, J. Poikonen, M. Laurinen, P. Nevalainen and J. Heikkonen, “Object Detection based on Multi-sensor Proposal Fusion in Maritime Environment”, The 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, US. 2. F. Farahnakian, P. Movahedi , J. Poikonen, E. Lehtonen, D. Makris and J. Heikkonen, “Comparative Analysis of Image Fusion Methods in Marine Environment”, Proceedings of the 13th edition of the IEEE International Symposium on RObotic and Sensors Environments (ROSE), 2019, Canada.