Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8-14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected.

Automated summary

This study developed a real-time pear fruit counter using YOLOv4 and Deep SORT algorithm, finding a balance between accuracy, speed, and computational cost, as well as providing a method for choosing the most suitable model for agricultural science applications. YOLOv4-CSP was identified as the most accurate model, while YOLOv4-tiny was deemed more suitable for applications requiring lower speed and computational cost.