An Improved YOLOv7-Based Model for Real-Time Meter Reading with PConv and Attention Mechanisms.

With the increasing complexity of the grid meter dial, precise feature extraction is becoming more and more difficult. Many automatic recognition solutions have been proposed for grid meter readings. However, traditional inspection methods cannot guarantee detection accuracy in complex environments. So, deep-learning methods are combined with grid meter recognition. Existing recognition systems that utilize segmentation models exhibit very high computation. It is challenging to ensure high real-time performance in edge computing devices. Therefore, an improved meter recognition model based on YOLOv7 is proposed in this paper. Partial convolution (PConv) is introduced into YOLOv7 to create a lighter network. Different PConv introduction locations on the base module have been used in order to find the optimal approach for reducing the parameters and floating point of operations (FLOPs). Meanwhile, the dynamic head (DyHead) module is utilized to enhance the attention mechanism for the YOLOv7 model. It can improve the detection accuracy of striped objects. As a result, this paper achieves mAP50val of 97.87% and mAP50:90val of 62.4% with only 5.37 M parameters. The improved model's inference speed can reach 108 frames per second (FPS). It enables detection accuracy that can reach ±0.1 degrees in the grid meter.

A spontaneous hyperglycaemic cynomolgus monkey presents cognitive deficits, neurological dysfunction and cataract.

Chronic hyperglycaemia is a chief feature of diabetes mellitus and complicates with many systematic anomalies. Non-human primates (NHPs) are excellent for studying hyperglycaemia or diabetes and associated comorbidities, but lack behavioural observation. In the study, behavioural, brain imaging and histological analysis were performed in a case of spontaneously hyperglycaemic (HGM) Macaca fascicularis. The results were shown that the HGM monkey had persistent body weight loss, long-term hyperglycaemia, insulin resistance, dyslipidemia, but normal concentrations of insulin, C-peptide, insulin autoantibody, islet cell antibody and glutamic acid decarboxylase antibody. Importantly, an impaired working memory in a delayed response task and neurological dysfunctions were found in the HGM monkey. The tendency for atrophy in hippocampus was observed by magnetic resonance imaging. Lenticular opacification, lens fibres disruptions and vacuole formation also occurred to the HGM monkey. The data suggested that the spontaneous HGM monkey might present diabetes-like characteristics and associated neurobehavioral anomalies in this case. This study first reported cognitive deficits in a spontaneous hyperglycaemia NHPs, which might provide evidence to use macaque as a promising model for translational research in diabetes and neurological complications.