Digital holographic imaging can capture a volume of a particle field and reconstruct three-dimensional (3D) information of the volume from a two-dimensional (2D) hologram. However, it experiences a DC term, twin-images, defocus images of other particles and noise induced by the optical system. We propose the use of a U-net model to extract in-focus particles and encode the in-focus particles as squares at ground truth z. Meanwhile, zero-order images, twin-images, defocused images of other particle and noise induced by the optical system are filtered out. The central coordinate of the square represents the lateral position of the particle, and the side length of the square represents the particle diameter. The 2D raw-reconstructed images generated from the pre-processed hologram by utilizing backward Fresnel propagation serve as the input of the network. A dense block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes the feature maps to all subsequent layers, thereby facilitating full characterization of the particles. The results show that the proposed U-net model can extract overlapping particles along the z-axis well, allowing the detection of dense particles. The use of that squares characterize particles makes it more convenient to obtain particle parameters.

Transformers have become increasingly prevalent in computer vision research, especially for object detection. To accurately and efficiently distinguish the stem end of pomelo from its black spots, we propose a hierarchical feature detector, which reconfigures the self-attention model, with high detection accuracy. We designed the combination attention module and the hierarchical feature fusion module that utilize multi-scale features to improve detection performance. We created a dataset in COCO format and annotated two types of detection targets: the stem end and the black spot. Experimental results on our pomelo dataset confirm that HFD’s results are comparable to those of state-of-the-art one-stage detectors such as YOLO v4 and YOLO v5 and transformer-based detectors such as DETR, Deformable DETR, and YOLOS. It achieves 89.65% mAP at 70.92 FPS with 100.34 M parameters.