Grape Instance De-Overlapping Occlusion Algorithm Based on Self-Supervised Learning
Conventional random occlusion algorithms used in generating synthetic occluded grape images often lead to data distortion,potentially rendering grape occlusion prediction ineffective.Therefore,this study proposes an occlusion data synthesis method suitable for grape occlusion prediction and further introduces a self-supervised grape instance de-occlusion prediction algorithm.During data synthesis,the proposed algorithm employs a proximity-based occlusion strategy to replace random occlusion methods for synthesizing different occluded instances from complete grape instances.Prior to the synthesis process,various preprocessing mechanisms are employed to control the sizes of mutually occluding grape instances,ensuring that the synthesized occluded grapes align with real-world conditions without distortion.Subsequently,the proposed approach splits occlusion prediction into mask reconstruction and semantic inpainting components.The study selects the corresponding synthetic data to train a generic Unet-based mask reconstruction network and a semantic inpainting network.To address the inability to predict complete instances owing to the limitations of instance segmentation cropping sizes,our algorithm fully considers both the occluded and occluder instances during data synthesis.The study introduces corresponding reconstruction and inpainting functions.In the occlusion prediction phase,an instance segmentation network,Pointrend,trained on an open-source architecture,the proposed mask reconstruction network,and a semantic inpainting network are sequentially applied to predict occluded grapes.When applied to the collected occlusion estimation dataset,the proposed algorithm achieves an Intersection-over-Union(IoU)value of 81.16％between the predicted occluded grape masks and ground truth annotations,outperforming other comparative methods.Experimental results demonstrate that the proposed synthesis algorithm and reconstruction framework are effective for grape occlusion prediction.
万方数据
维普
