An accurate detection and segmentation model of obscured green fruits

Abstract

Accurate detection and segmentation of obscured green fruits is a key factor in the efficiency of the harvesting robot. In the real and complex orchard environment, there are many influencing factors such as illumination changes, occlusion of branches and leaves, weather, etc., which bring difficulties to the work of picking robots. Therefore, higher requirements are put forward for accurate detection and segmentation algorithms of machine vision. An accurate detection and segmentation method for obscured green fruit based on a fully convolutional one-stage (FCOS) object detection model is proposed. First, the feature pyramid network (FPN) in FCOS is replaced by residual feature pyramid network (RFPN), which improves the detection accuracy of green fruits with different sizes. Next, as the boundaries of fuzzy fruits are often not accurately identified, a two-layer convolutional block attention network is connected to a modified FCOS to recover the edges of incomplete green fruits. The experimental results show that the detection and segmentation accuracy of green fruit reaches 81.2% and 85.3% on Apple dataset. On the Apple-ape dataset, the detection accuracy and segmentation accuracy reached 77.2% and 79.7%, respectively. The Params of the model is 39.7 M, and the floating point operations (FLOPs) are 169.8G, which has great advantages over other ten advanced models in terms of accuracy and complexity. The model also improves the working efficiency of orchard harvesting robots and lays the foundation for the further development of intelligent agriculture.