Rapid dataset generation methods for stacked construction solid waste based on machine vision and deep learning.

The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high. To rapidly and automatically generate datasets for stacked construction waste, an acquisition and detection platform was built to automatically collect different groups of RGB-D images for instances labeling. Then, based on the distribution points generation theory and data augmentation algorithm, a rapid-generation method for synthetic construction solid waste datasets was proposed. Additionally, two automatic annotation methods for real stacked construction solid waste datasets based on semi-supervised self-training and RGB-D fusion edge detection were proposed, and datasets under real-world conditions yield better models training results. Finally, two different working conditions were designed to validate these methods. Under the simple working condition, the generated dataset achieved an F1-score of 95.98, higher than 94.81 for the manually labeled dataset. In the complicated working condition, the F1-score obtained by the rapid generation method reached 97.74. In contrast, the F1-score of the dataset obtained manually labeled was only 85.97, which demonstrates the effectiveness of proposed approaches

Experimental Study on Limestone Cohesive Particle Model and Crushing Simulation

This study investigates the effect of impact velocity and particle size on crushing characteristics. We use a discrete-element method simulation and construct cohesive limestone particles with internal microinterfaces and cracks for impact crushing experimentation. The simulation model follows the same process as the impact crushing experiment. Results show that, after crushing at impact velocities of 30 and 40 m/s, the simulated particle-size distribution curve matches experimental results as closely as 95%. For different particle sizes, results are more than 90% in agreement. These results indicate the feasibility of the cohesive-particle crushing simulation model. When the particle size is 15 mm, an approximate linear relationship exists on impact velocity and crushing ratio. For a constant impact velocity, the particle size of 18 mm results in the maximum crushing ratio

Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification

Genomic and Phylogenetic Characterization of Novel, Recombinant H5N2 Avian Influenza Virus Strains Isolated from Vaccinated Chickens with Clinical Symptoms in China

Infection of poultry with diverse lineages of H5N2 avian influenza viruses has been documented for over three decades in different parts of the world, with limited outbreaks caused by this highly pathogenic avian influenza virus. In the present study, three avian H5N2 influenza viruses, A/chicken/Shijiazhuang/1209/2013, A/chicken/Chiping/0321/2014, and A/chicken/Laiwu/0313/2014, were isolated from chickens with clinical symptoms of avian influenza. Complete genomic and phylogenetic analyses demonstrated that all three isolates are novel recombinant viruses with hemagglutinin (HA) and matrix (M) genes derived from H5N1, and remaining genes derived from H9N2-like viruses. The HA cleavage motif in all three strains (PQIEGRRRKR/GL) is characteristic of a highly pathogenic avian influenza virus strain. These results indicate the occurrence of H5N2 recombination and highlight the importance of continued surveillance of the H5N2 subtype virus and reformulation of vaccine strains