As flowchart images become diverse and complex, existing flowchart recognition methods no longer achieve satisfactory recognition accuracy, particularly for images that contain rarely used symbols and texture backgrounds. Existing deep-learning-based object detectors and line segment detectors are promising in recognizing symbols and connecting edges separately. However, using two separate detectors for symbol and edge detection will inevitably cause unnecessary training and inference costs. Moreover, the insufficient volume and diversity of available dataset further limit the overall recognition accuracy. To address these issues, this paper proposes an end-to-end multi-task network FR-DETR (Flowchart Recognition DETection TRansformer) and a new dataset for precise and robust flowchart recognition. FR-DETR comprises a CNN backbone and a shared multi-scale Transformer structure with two prediction heads for symbol detection and edge detection respectively. The multi-scale Transformer encodes and decodes feature maps with different resolutions to jointly detect symbols and edges in a coarse-to-fine refinement process. The coarse stage uses features with low resolution and suggests candidate regions that contain potential targets for the fine stage to produce accurate predictions using features with high resolution. At each stage, every task detects targets using shared features and its respective prediction head. A new dataset is constructed to provide more symbol types and complex backgrounds for network training and evaluation. It contains more than 1000 machine-generated flowchart images, 25K+ symbol instances with nine categories, and 20K+ line segments. The experiments show that FR-DETR achieves an overall precision and recall of 94.0% and 93.1% on the proposed dataset, and 98.7% and 98.1% on the CLEF-IP dataset, respectively, which all outperform the prior methods.
This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.
The olfactory system is essential for honeybees to adapt to complex and ever-changing environments and maintain cohesiveness. The Eastern honeybee Apis cerana is native to Asia and has a long history of managed beekeeping in China. In this study, we analysed the antennal transcriptomes of A. cerana workers and drones using Illumina sequencing. A total of 5262 differentially expressed genes (DEGs) (fold change > 2) were identified between these two castes, with 2359 upregulated and 2903 downregulated in drones compared with workers. We identified 242 candidate olfaction-related genes, including 15 odourant-binding proteins (OBPs), 5 chemosensory proteins (CSPs), 110 odourant receptors (ORs), 9 gustatory receptors (GRs), 8 ionotropic receptors (IRs), 2 sensory neuron membrane proteins (SNMPs) and 93 putative odourant-degrading enzymes (ODEs). More olfaction-related genes have worker-biased expression than drone-biased expression, with 26 genes being highly expressed in workers’ antennae and only 8 genes being highly expressed in drones’ antennae (FPKM > 30). Using real-time quantitative PCR (RT-qPCR), we verified the reliability of differential genes inferred by transcriptomics and compared the expression profiles of 6 ORs (AcOR10, AcOR11, AcOR13, AcOR18, AcOR79 and AcOR170) between workers and drones. These ORs were expressed at significantly higher levels in the antennae than in other tissues (p < 0.01). There were clear variations in the expression levels of all 6 ORs between differently aged workers and drones. The relative expression levels of AcOR10, AcOR11, AcOR13, AcOR18 and AcOR79 reached a high peak in 15-day-old drones. These results will contribute to future research on the olfaction mechanism of A. cerana and will help to better reveal the odourant reception variations between different biological castes of honeybees.
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