Lightweight and Efficient Attention-based CNN Models for In-field Strawberry Instance Segmentation

Accurately segmenting strawberries within real-world production settings not only helps the development of automated harvesting robots but also allows for precise calculation of the number and size of strawberries, providing accurate yield information for agricultural planning and resource optimization. This paper proposes lightweight and efficient CNN models specifically designed for strawberry instance segmentation, consisting of an efficient self-attention-based backbone, a feature pyramid network (FPN), and a decoder with an instance branch and a mask branch. The proposed models surpass the original and simplified Mask R-CNN with significant 21.46 and 22.97 AP gains respectively, with the Base backbone achieving the highest AP of 70.22. Additionally, our models demonstrate efficiency by requiring much fewer parameters (17.42M) and floating-point operations (78.3G) compared to Mask R-CNN (35.08M / 877.4G), making them suitable for deployment on devices with limited computational resources.