Background: Fast identification of damaged soybean seeds has undeniable importance in seed sorting and food quality. Mechanical vibration is generally used in soybean seed sorting, but this can seriously damage soybean seeds. The convolutional neural network (CNN) is considered an effective method for location and segmentation tasks. However, a CNN requires a large amount of ground truth data and has high computational cost.
Results: First, we propose a self-supervision manner to automatically generate ground truths, which can theoretically create an almost unlimited number of labeled images. Second, instead of using popular CNNs, a novel invertible convolution (involution)-enabled scheme is proposed by using the bottleneck block of the residual networks. Third, a feature selection feature pyramid network (FS-FPN) based on involution is designed, which selects features more flexibly and adaptively. We further merge involution-based backbones and FS-FPN into a unified network, achieving an end-to-end seed location and segmentation model; the best mean average precision of location and segmentation achieved was 85.1% and 81% respectively.
Conclusion: The experimental results demonstrate that the proposed method greatly improves the performance of the baseline network with faster speed and fewer parameters, enabling it to detect soybean seeds more effectively. © 2022 Society of Chemical Industry.
Keywords: feature pyramid network; high-throughput soybean seeds; invertible neural networks; location; segmentation.
© 2022 Society of Chemical Industry.