VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection.

Abstract

Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network's capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.

Plant phenotyping relevance

イチゴ花の検出を目的とする画像解析モデルを開発し、データセット上でアブレーション、比較、性能評価を行っており、植物器官の取得・抽出法が研究の中心である。

Code and data availability

The authors' self-constructed strawberry flower dataset (3388 labeled images used for all VM-YOLO experiments) is explicitly made publicly available via a Google Drive link in the Data Availability statement. The other listed datasets (Global Wheat Head 2020, CropAndWeed, Strawberry Disease) are cited prior public sets