Speaker
Description
In automatic object detection, reliably counting objects remains challenging, particularly in scenarios with densely packed objects, overlapping instances, large scene variability, or multi-class cases.
Common evaluation metrics for object detection are based on Intersection over Union (IoU) and do not directly measure the correctness of the number of detected objects. Consequently, a model may achieve high detection scores while substantially overestimating or underestimating the true object count. This discrepancy limits the usefulness of standard detection metrics in applications where accurate quantitative assessment is crucial.
Several approaches for automatic object counting have been proposed in the literature, including post-processing of detector outputs (Chattopadhyay et al., 2017), density-map regression (Sindagi & Patel, 2018), and hybrid detection–regression methods (Liu et al., 2019). Each of these approaches comes with specific requirements regarding data volume, annotation detail, or robustness to detector inaccuracies.
Our work was motivated by practical challenges encountered during the analysis of microscopic images of pollen grains from selected plant taxa. While the detector used in the study produced high-quality detections according to standard metrics, it did not provide a reliable estimate of the actual number of grains. The gap between seemingly strong detection performance and inaccurate object counts highlighted a methodological limitation and inspired the development of a more robust metric for counting evaluation.
Therefore, we propose the evaluation metrics that take into account the detector’s confidence score and cases where multiple labels have been assigned to a single object. Our method may be particularly useful in applications where false positive counts should be limited. The work includes a case study comparing the proposed approach to traditional evaluation metrics.
References:
• Chattopadhyay, P., Vedantam, R., Selvaraju, R. R., Batra, D., & Parikh, D. (2017). Counting Everyday Objects in Everyday Scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1135–1144).
• Liu, L., Jiang, J., Jia, W., Amirgholipour, S., Zeibots, M., & He, X. (2019). DENet: A Universal Network for Counting Crowd with Varying Densities and Scales. arXiv preprint.
• Sindagi, V. A., & Patel, V. M. (2018). A Survey of Recent Advances in CNN‑Based Single Image Crowd Counting and Density Estimation. Pattern Recognition Letters, 107, 3–16.
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