Improving weighted Hausdorff distance loss for small object centroid detection in infra-red and RGB imagery

Babak Ebrahimi Soorchaei; Arisa Kitagishi; Abhijit Mahalanobis

doi:10.1117/12.2619044

31 May 2022 Improving weighted Hausdorff distance loss for small object centroid detection in infra-red and RGB imagery

Babak Ebrahimi Soorchaei, Arisa Kitagishi, Abhijit Mahalanobis

Author Affiliations +

Proceedings Volume 12096, Automatic Target Recognition XXXII; 1209609 (2022) https://doi.org/10.1117/12.2619044
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Detection of moving objects at very long distances using infrared sensors is a challenging problem due to the small object size and heavy background clutter. To mitigate these problems, we propose to employ a convolutional neural network (CNN) with mean squared error (MSE) loss and show that this network detects the small objects with fewer false alarm rate than frame differencing methods. Furthermore, we modify a U-net architecture (introduced in1 ) and use both a weighted Hausdorff distance (WHD) loss and MSE loss which jointly achieve higher recall and lower false alarm rate. We compare our proposed method with state-of-the-art methods on a publicly available dataset of infrared images from Night Vision and Electronic Sensors directorate (NVESD) for the detection of small moving targets. We also show the effectiveness of our loss function on Mall dataset reported in.1 Our method achieves 5% and 2% more recall on the NVESD and Mall datasets, respectively. Furthermore, our method also achieves 0.3 per frame and 1 per frame fewer false alarm rate on these datasets, respectively.

Conference Presentation

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Babak Ebrahimi Soorchaei, Arisa Kitagishi, and Abhijit Mahalanobis "Improving weighted Hausdorff distance loss for small object centroid detection in infra-red and RGB imagery", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209609 (31 May 2022); https://doi.org/10.1117/12.2619044

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