Multi-object tracking using Deep SORT and modified CenterNet in cotton seedling counting

Abstract

Accurate plant density information is important for crop yield and quality. In general, human has to estimate plant density either in field or with accessory equipment, which is time-consuming and inaccurate. In this work, multi-object tracking method based on tracking-by-detection strategy was developed to automatically count cotton seedlings. Videos were collected 0.5 m above cotton seedlings, and analyzed to train object detection model and evaluate counting accuracy with a separate dataset (TAMU2015-ID). An advanced anchor-free object detection model was developed using CenterNet to detect cotton seedling and extract its identity embedding. The localization and identity information were fused based on Deep SORT for data association. The object detection model outperformed Faster R-CNN model with an F1 score of 0.982 (IOU0.5) and 0.937 (IOU0.8), and an average precision of 0.9901 (IOU0.5) and 0.8998 (IOU0.8). The counting results were fitted to ground truth with a R2 of 0.967 and RMSE of 0.394. We evaluated the method on TAMU2015-ID to get a R2 of 0.99 and RMSE of 0.8.