Abstract:
The development of strawberry picking robot and the information of strawberry growth status play important roles in the process of automated strawberry planting. The segmentation of strawberry image directly affects the strawberry picking robot real-time operation and the analysis on strawberry growth status. All of these require that the strawberry segmentation algorithms should be suitable for different light and dark environments and attain better segmentation effect on the strawberry in different maturity. However, most of the existing algorithms on strawberry image segmentation only considered the segmentation in fixed background or fully mature such that they could not achieve the multi-level classification and the detection under occlusion. This paper proposes a strawberry image segmentation method by combining salient region detection. The contrast-limited adaptive histogram equalization processing method is firstly adopted to increase the contrast between the strawberry and the background under different light intensities. It is well known that the shallow side output contains rich details and deep side output can attain accurate regional position. By means of these above features, this paper applies the Holistically-Nested salient object detection method with short connections to produce the dense and accurate salient areas. And then, these salient areas are segmented as the foreground of Grabcut so as to realize the accurate segmentation of strawberry image. Finally, it is shown from the experiment results that the proposed algorithm can obtain stable and accurate segmentation results under the occlusion and different light and dark environments.