YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism

Abstract

The automatic cultivation of corn has become a significant research focus, with precision equipment operation being a key aspect of smart agriculture’s advancement. This work explores the tracking process of corn, simulating the detection and approach phases while addressing three major challenges in multiple object tracking: severe occlusion, dense object presence, and varying viewing angles. To effectively simulate these challenging conditions, a multiple object tracking dataset using simulated corn was created. To enhance accuracy and stability in corn tracking, an optimization algorithm, YOLOv8MS, is proposed based on YOLOv8. Multi-layer Fusion Diffusion Network (MFDN) is proposed for improved detection of objects of varying sizes, and the Separated and Enhancement Attention Module (SEAM) is introduced to tackle occlusion issues. Experimental results show that YOLOv8MS significantly enhances the detection accuracy, tracking accuracy and tracking stability, achieving a mean average precision (mAP) of 89.6% and a multiple object tracking accuracy (MOTA) of 92.5%, which are 1% and 6.1% improvements over the original YOLOv8, respectively. Furthermore, there was an average improvement of 4% in the identity stability indicator of tracking. This work provides essential technical support for precision agriculture in detecting and tracking corn.

Keywords

• corn
• multiple object track
• feature fusion
• attention mechanism
• YOLOv8