Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor

This article presents a new method for strawberry detection for use in a strawberry harvesting robot. The method is based on a histogram of oriented gradients (HOG) descriptor associated with a support vector machine (SVM) classifier. The detection involves two stages. First, strawberry-like regions are detected from HSV (hue, saturation, value) colour information. The HOG descriptor, calculated using five regions of interest (ROI), is input to an HOG/SVM classifier, which detects the strawberries. The performance of the model was verified by experiments. The vector sizes were effectively reduced and a higher detection speed was achieved without compromising accuracy (relative to conventional approaches). The proposed classifier achieves high detection accuracy (87%) in a reasonable run time, and can appropriately handle slightly overlapping strawberries.

► New method to detect strawberries for strawberry harvesting robot was presented. ► Two-stage approach combined the HSV colour information and HOG descriptor. ► Strawberry region divided into 5 ROI to deal with slightly overlapping fruit. ► Optimal parameters sped up detection process while maintaining comparably accuracy.