Multi-object detection and tracking algorithm for fry counting based on DV-YOLO and FryMOT

Abstract

The fry counting is essential in aquaculture. Precisely counting the fry can be used to estimate the quantity of aquaculture products. Deep-learning-based multi-object tracking and counting technologies have received increasing attention in recent years. However, detecting and tracking fry in complex scenarios remains challenging due to fry’s high density and irregular movements. To address these issues, we introduce a deformable convolutional block (DCB) to capture more fine-grained features and vertex distance intersection over union (VDIoU) loss for better bounding box regression localization. Thus, our model can efficiently deal with the high-density scenarios caused by neighboring or overlapping fry. Furthermore, we introduce the enlarged intersection over union (IoU) to address the issue of unmatched tracking caused by the irregular movements of the fry. The complementary experimental results show that our methods achieve state-of-the-art performance.