ARTD-Net: Anchor-Free Based Recyclable Trash Detection Net Using Edgeless Module.

Due to the sharp increase in household waste, its separate collection is essential in order to reduce the huge amount of household waste, since it is difficult to recycle trash without separate collection. However, since it is costly and time-consuming to separate trash manually, it is crucial to develop an automatic system for separate collection using deep learning and computer vision. In this paper, we propose two Anchor-free-based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless modules: ARTD-Net1 and ARTD-Net2.

Experimental validation for the determination of particle positions by the correlation coefficient method in digital particle holography.

The feasibility and the accuracy of the correlation coefficient (CC) method for the determination of particle positions along the optical axis in digital particle holography were verified by validation experiments. A translation system capable of high precision was used to move the particle objects by exact known distances between several different positions. The particle positions along the optical axis were calculated by the CC method and compared with their exact values to obtain the errors of the focus plane determination.

Application of the correlation coefficient method for determination of the focal plane to digital particle holography.

The correlation coefficient (CC) method, which was proposed by our research group, is applied to digital particle holography to locate the focal plane of particles. It uses the fact that the CC is maximum at the focal plane. The factors influencing this method are discussed with a numerical simulation of holograms.