No abstract

Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, is an extension of image classification. In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical instrument positioning in medical navigation surgery, etc. COVID-19, a novel coronavirus outbreak at the end of 2019, poses a serious threat to public health. Many countries require everyone to wear a mask in public to prevent the spread of coronavirus. To effectively prevent the spread of the coronavirus, we present an object detection method based on single-shot detector (SSD), which focuses on accurate and real-time face masks detection in the supermarket. We make contributions in the following three aspects: 1) presenting a lightweight backbone network for feature extraction, which based on SSD and spatial separable convolution, aiming to improve the detection speed and meet the requirements of real-time detection; 2) proposing a Feature Enhancement Module (FEM) to strengthen the deep features learned from CNN models, aiming to enhance the feature representation of the small objects; 3) constructing COVID-19-Mask, a large-scale dataset to detect whether shoppers are wearing masks, by collecting images in two supermarkets. The experiment results illustrate the high detection precision and realtime performance of the proposed algorithm.

Deep learning has recently shown great potential in computational imaging. Here, we propose a deep-learning-based reconstruction method to realize the sparse-view imaging of a fiber internal structure in holographic diffraction tomography. By taking the sparse-view sinogram as the input and the cross-section image obtained by the dense-view sinogram as the ground truth, the neural network can reconstruct the cross-section image from the sparse-view sinogram. It performs better than the corresponding filtered back-projection algorithm with a sparse-view sinogram, both in the case of simulated data and real experimental data.

To accurately identify apples from the complex background in the natural environment and help the apple harvesting robot harvest apples accurately, an improved apple image segmentation algorithm based on Deeplabv3 framework is proposed, which is named as AppleDNet. Using the famed Deeplabv3 algorithm, combined with Atrous convolution, Depthwise separable convolution and transfer learning, not only can achieve more accurate segmentation results but also improve segmentation speed. In addition, the traditional image filtering algorithm is adopted to obtain a smoother segmentation image and effectively eliminate the image stitching trace. The experimental results demonstrate that the performance of the proposed method is superior to the original Deeplabv3 and other popular mainstream image segmentation algorithms, with an overall accuracy of 97.90%, which has certain significance and advantages in practice.