The Bell-Bloom magnetometer is an instrument for measuring weak magnetic fields that are widely used in geophysical exploration, earthquake monitoring, natural disaster monitoring, and other fields. In geophysical exploration, the magnetometer can detect changes in underground materials; and in magnetic field monitoring, it can accurately detect magnetic field anomalies caused by earthquakes. Relaxation is a crucial characteristic of the Bell-Bloom magnetometer, and unclear relaxation information can hinder the design and improvement of the Bell-Bloom magnetometer, thereby affecting its potential applications. This study proposes an intelligent algorithm called relaxation time of free precession signal (RT-FPS) for measuring the relaxation time of the Bell-Bloom magnetometer parameters to address these issues. Experimental results demonstrate the algorithm’s superior convergence efficiency, fitting accuracy, and noise robustness. Moreover, the algorithm exhibits rapid convergence and high computational accuracy with a minimum sum of squared residuals$1.8386 \times 10^{-11} \text { s}^{2}$. The algorithm is robust against different types of noise and is minimally affected by data quality, with minimum errors of 0.1 ms and$0.71~\mu \text{s}$for$T_{1}$and$T_{2}$, respectively. This study can enhance the performance of the Bell-Bloom magnetometer and its potential applications in magnetic anomaly detection and geomagnetic field monitoring. Our shareable code and data sources are available athttps://github.com/baicaidezhenshi/RT-FPS-DATA.git.

The free precession style Bell–Bloom atomic magnetometer is widely used in geophysical exploration, earthquake monitoring, and natural disaster monitoring to obtain magnetic field information by measuring the Larmor frequency of the free precession signal. However, the free precession signal complexity makes it challenging to accurately acquire the Larmor frequency using conventional frequency measurement methods, limiting its applicability. This study proposes a magnetic information of free precession signal data (MI-FPD) algorithm for measuring the Larmor frequency of the free precession signal in the Bell–Bloom atomic magnetometer. The MI-FPD algorithm accurately determines the Larmor precession frequency based on free precession signal data, providing precise magnetic field information. The algorithm outperforms established algorithms regarding convergence efficiency, accuracy, and noise resistance, with an optimal determination coefficient$R^{2}$of 0.99984, an optimal range of less than 1.04 nT, and an optimal root mean square error (RMSE) of 189 pT. These results demonstrate the effectiveness of the proposed algorithm in accurately obtaining magnetic field information in the free precession style Bell–Bloom atomic magnetometer. This capability enables widespread application of the free precession style Bell–Bloom atomic magnetometer in geomagnetic monitoring and disaster early warning within the geoscience domain. The shareable data are available athttps://github.com/baicaidezhenshi/MI-FPD-DATA.git.

Summary The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.

Summary Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.

Abstract Product surface quality inspection based on machine vision has been paid more and more attention in modern industrial production. Multidimensional feature information fusion also plays an important role in the detection rate and accuracy of steel plate defects. Based on the machine vision based defect detection of steel plate surface, this article mainly studies the collection, extraction and fusion of multidimensional feature information. The visual imaging system, the surface defect detection technology based on vision, the depth camera and the light source type are briefly reviewed. At the same time, the roadmap and related methods of multidimensional feature information acquisition system are studied. Finally, the multidimensional fusion defect detection technology is prospected and summarized.