Precise Counting
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Precise Counting
I would like to start the topic of precise instance counting in 2D images. It would seem that it is somewhat covered by simple classification using CNNs and/or object detection, however in reality the results provided by both of the former rarely provide an exact enough answer. Are there smart ways to overcome the limitations of the default go-to methods and arrive at accuracy above 99.9% ? This is the question I will try to answer. At first I will just keep adding literature here, before I start writing up some conclusions.
Task Architecture Best Metric References wheat heads CNN MAE 3.85; RMSE 5.19 [1] corn plants YoloV3 Accuracy 0.9866 [2] pistachios RetinaNet + algorithm Accuracy 0.9475 [3] fish CNN Accuracy 0.9755 [4] multiple (a review) multiple (a review) nMAE 0.05 - 0.1 [5] crops, cells, colonies YOLOv3 F1 0.947 [6] plants, UAVs MixNet R² 0.9396 / 0.9875 [7] plant leaves Recurrent Attention SBD 0.849 [8] todo Automated Counting of Colony Forming Units Using Deep Transfer Learning From a Model for Congested Scenes Analysis - [9]
Non-DL:
Principal Component-Based Image Segmentation - [10]