PD-SegNet: Semantic Segmentation of Small Agricultural Targets in Complex Environments

Abstract

The agricultural scene is a typical unstructured scene, which is intricate and heavily affected by sunlight, weather, and other factors. Agricultural segmentation targets are generally small in size and heavily obstructed. At the same time, image segmentation in agricultural scenes has strong application scenarios, such as blooming intensity estimation, which refers to the estimation of the density and intensity of blooms, fruit yield estimation, fruit harvesting positioning, and so on. Currently, CNNs dominate semantic segmentation of agricultural scenes due to the significant computational constraints of using the Transformer module. However, CNNs have several disadvantages, such as limited effective receptive fields and the inability to capture global information, which significantly reduce their segmentation accuracy in complex agricultural scenes. In addition, the simple upsampling process used in CNNs can result in blurred segmentation edges and inferior performance. This paper presents a new semantic segmentation algorithm based on SegFormer: PD-SegNet(Powerful Decoder SegFormer Network), which balances accuracy and computational efficiency and combines dynamic kernel self-renewal with edge-aware optimization. The proposed algorithm demonstrates outstanding performance in two typical agricultural scenarios: apple blossom and apple fruit segmentation detection and sets a new state-of-the-art (SOTA) on the MinneApple Apple segmentation dataset. Experimental results demonstrate that the proposed method outperforms the baseline method in the segmentation of complex small targets. This algorithm can optimize the semantics segmentation of small targets in complex scenes and contributes to the development of smart agriculture.

Keywords

• Smart agriculture
• deep learning
• semantic segmentation
• object detection
• transformer
• dynamic kernel