# Weak-Edge Extraction of Nuclear Plate Fuel Neutron Images at Low Lining Degree
## 1 Introduction  
Nuclear energy has become a significant clean energy source in mainstream energy development. It has low resource consumption, low environmental impact, and high supply capacity. Fission reactors are the most common means of nuclear energy production. The core component of a reactor is the fuel element, whose structural properties and performance directly influence the reactor's base state and safe operation [1]. In particular, defects in the structure of the fuel element can cause defect migration, breakage, and a lack of physical integrity, resulting in the leakage of radioactive material [2, 3, 4]. Differences in enrichment distribution can cause power fluctuations, high local temperatures, and even cladding breakage, damaging the pellet and resulting in nuclear accidents [5, 6]. Therefore, during fuel element development, production, and service, the structural defects, nuclide enrichment, and distribution uniformity of fuel elements need to be detected to ensure that the quality standards are met and the safe operation of the reactor is guaranteed. Neutron imaging [7] is a nondestructive testing (NDT) technology used for the analysis and determination of defects and components of a sample. It has the following advantages over X-ray imaging: high penetration and nuclide-sensitive properties, making neutron imaging suitable for fuel element NDT in structural defects and pellet nuclide enrichment. During fuel nuclide enrichment NDT, since the nuclear fuel pellet is sealed in the cladding, the transmission imaging result is a superposition of the two parts, so the attenuation of neutrons by the cladding is a crucial interference term. Extracting and separating the cladding and pellet edges to reduce the effect of cladding using image processing techniques is vital. However, the low neutron cross-section of the cladding material (e.g., aluminum and zirconium) results in poor contrast at the cladding edge, which is almost invisible in the original image. The clarity of the cladding edge is much lower than that of the pellet edge. Additionally, the contours of targets are further blurred by noise from the imaging environment, which makes edge extraction even more difficult. Therefore, a suitable edge extraction algorithm must be developed to accurately separate fuel element cladding from the pellet and determine more precise nuclide enrichment. Researchers have proposed various edge detection methods, which fall into four categories [8]: gray-scale gradient-based, wavelet transform-based, morphology-based, and machine learning-based. As suggested by [PERSON] et al., traditional gradient-based operators can be divided into two types [9]. One employs the maximum absolute values of the first-order derivative/gradient methods (e.g., Roberts, Prewitt, and Sobel). The other is the zero crossings of second-order derivative/gradient-based methods, such as the Laplacian, the Gaussian difference, and the Laplacian of Gaussian distribution methods. According to the paper [10], the above operators exhibit high-frequency amplification qualities, making them noise-sensitive. In addition, the Canny [11] detector is currently the optimal method among gradient-based algorithms because of its better signal-to-noise ratio (SNR) and greater accuracy in edge detection. The wavelet transform algorithm [12] performs wavelet transforms on images at different scales. At small scales, the edge detail information is rich and the localization accuracy is high, but the anti-disturbance ability is poor. At large scales, the localization accuracy is low and the anti-disturbance ability is good; the results of edge images are fused at each scale. This considers localization accuracy and anti-disturbance ability, but the algorithm's complexity is high. The morphology-based algorithm uses structural elements to move continuously through the image to analyze the image and extract different features by selecting structural elements for other operations, such as opening and closing.