Detection of In-Perceptible Fruits Bearing on Trees from Inter-Spaced Images
Keywords:
Fine-Tuning, Fruit Detection, Mask-RCNN Object Detection, Region Based Detectors, Single Shot Detectors, YOLOAbstract
The detection of fruits is a mainstream application taken into account for Single Shot Detectors as well as Region Based Detectors. The problem can be taken on other dimension by making a shift inclined towards the real world application where the detection of fruits when bearing on the trees are done. The dataset for such a problem needs to be gathered in a custom fashion, which we were able to do successfully. Apart from this the annotations for data were also made and famous state of-the-art Object Detection models were used for performance mapping using YOLO.v3, YOLO.v4 and Mask-RCNN. These models are fine-tuned to meet the needs of the dataset and metric of mean average precision comparison is shown. The loss metrics for these models in form of their comparisons is also provided in the results section. The result section also highlights a detailed prediction yielded by the different models on different testing based images respectively
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