steel wire rope images free sample

The boom of a wheeled crane and a rope for lifting loads in closeup heavy industrial mobile crane view of a mobile crane on a construction site with cable control and a telescopic boom

Barbed wire fencing strong sharply pointed element twisted around art pattern industrial barbwire protection concept design modern metallic sharp element for area protection

Stainless steel wire rolls in the construction site. closeup of metal steel reinforced rod for concrete in store. construction industry concept backgroundPREMIUM

Coil large wire rope sling or cable sling drum reels stocked in store. steel wire cable or rope for heavy industrial use, wire rope texture, selective focus.PREMIUM

Infinity sign made of wire rope, metal hawser, steel cable. industrial technology and machine engineering symbol. realistic vector illustration isolated on white background.PREMIUM

Bundle of steel coils being loading discharging by ship crane in port terminal, handle by gang of stevedore labor, shipment cargo in transition from land and sea transport servicesPREMIUM

Infinity sign made of wire rope, metal hawser, steel cable. power engineering and nautical industry symbol. 3d render isolated on white background.PREMIUM

Steel wire coil with sunlight sky. metal steel reinforced for concrete construction. iron wire for construction industry. spool or roll of silver steel wire. metal materials. rusty steel wire coil.PREMIUM

Loops of metal cable close up on white background isolated, space for text. texture of heavy duty steel cable, steel wire rope or steel sling. use for industrial or construction, advertising backgroundPREMIUM

In the field of surface defect detection, traditional manual inspection has the disadvantages of low accuracy, poor real-time defect detection, low efficiency, and high labor intensity. Machine-vision-based methods have the advantages of non-contact, high real-time, and no manual participation [1], these methods are increasingly being applied in modern industries [2,3,4,5,6]. In this study, we investigated machine-vision-based methods for detecting defects on the surface of steel wire ropes, using sealed wire ropes as the research object. Sealed wire ropes are widely used in aerial ropeway systems in mines or at scenic spots for transporting goods or carrying people. This type of wire rope is usually twisted from one to three layers of shaped steel wire wrapped with multiple strands of round heart steel wire, with a tight structure, a smooth surface, no rotation, and good sealing characteristics, with the section structure and appearance shown in Figure 1a,b. The sealed wire rope surface defects occur mainly because the wire rope is pulled apart by the pressure of the surface rope strand fracture to form fracture defects. As a result of the force exerted by the weight of goods and people, local broken wire develops into a concentrated broken strand in a short period. When the concentrated broken strand exceeds the scrap standard, the whole rope needs to be replaced, leading to a short life cycle. At the same time, in this short life cycle, the quantity of fracture defect data is small, which makes it difficult to use supervised learning methods to detect defects [7].

Main types and structure of wire rope. (a) Section of sealing wire rope; (b) Appearance of sealing wire rope; (c) Section of unsealed wire rope; (d) Appearance of unsealed wire rope.

There is little literature on machine-vision-based defect detection for sealed wire ropes, but researchers have carried out a lot of work on surface defect detection for non-sealed ordinary wire ropes. A non-sealed wire rope plays an important role in lifting and traction systems. The difference between sealed wire rope and non-sealed wire rope lies in the composition and twisting method of wire rope. The main performance is that the appearance of non-sealed wire rope has finer texture than that of sealed wire rope, and these textures are more prone to wear, broken wire, corrosion, and other defects. The section structure and appearance are shown in Figure 1c,d.

Zhou et al. [8,9,10,11] studied in-depth non-sealed wire ropes and proposed a wear detection method based on a deep convolutional neural network that involved training a large number of labeled wear sample images to detect the wear degree of wire ropes. To address the influence of light noise, using texture features, the uniform local binary pattern operator (u-LBP) and principal component analysis methods were proposed to detect the wire rope defect area. In addition, to solve the wire rope surface defect classification problem, an optimized support vector machine method based on uniform local binary features and grayscale co-occurrence moment features was proposed. Xh et al. [12] proposed a convolutional-neural-network-based intelligent method for detecting wire rope damage by training a model with a large number of damage sample images. Shi et al. [13] used the infrared measurement method to determine wire rope wear using the Canny edge detection method with an infrared camera. From the perspective of structural modeling, Wacker et al. [14] proposed a combination of structure- and appearance-enhanced wire rope anomaly detection to design probabilistic appearance models to detect defects. To achieve better-supervised learning of defect models, Platzer et al. [15] proposed a new strategy for wire rope defect localization using hidden Markov models. To solve the problem of wire rope twist distance measurement, Vallan et al. [16] established a mathematical model of a wire rope profile and a vision-based twist distance measurement technique for a metal wire rope. Dong et al. [17] detected wire rope defects by extracting texture features such as the smoothness and entropy of the wire rope surface. Ho et al. [18] combined image enhancement techniques with principal component analysis (PCA) for detecting wire rope surface defects. The above methods have achieved good results in the surface defect detection of unsealed steel wire rope, but they depend on a large amount of defect data and are not suitable for the defect detection of sealed steel wire rope.

When the outdoor camera is fixed, the changing area in the fixed scene is extracted, and the dynamic background modeling method is usually used. The background modeling process is shown in Figure 2. The background model is dynamically constructed for the real-time image sequence, and the scene change area, that is, the foreground area, is obtained through the difference between the dynamic background and the real-time image frame. For the continuously collected outdoor sealed steel wire rope images, the change of pixel gray in the surface area of steel wire rope is similar to the application scene of the background modeling method. In the time domain, the pixels in the image sequence of wire rope surface area at the same position may be rope strand edge, rope strand surface, oil sludge, or fracture, but the gray levels of pixels in one type are similar in the time domain. At the same time, the gray levels of rope strand edge and fracture are similar, the gray levels of wire rope surface and oil sludge are similar, but the gap gray level is significantly different from that of wire rope surface; In the spatial domain, the gray level of the rope strand area pixels or the rope strand edge area pixels in the wire rope surface area image is similar. According to the above wire rope image characterization, the change model of a single-pixel in the image can be established, and the defect area on the wire rope surface can be extracted by using a methodology similar to dynamic background modeling. The classical dynamic background modeling methods such as the visual background extractor (VIBE) method proposed by O. Barnich et al. [19,20], which established a random replacement background model for each pixel; an adaptive background mixing model based on KNN (k-nearest neighbor) proposed by Stauffer C et al. [21], which modeled each pixel and realized foreground segmentation by using the idea of nonparametric probability density estimation and KNN classification; the MOG2 method of Gaussian mixture model proposed by Zivkovic Z [21,22,23], which realized dynamic background modeling by using parametric probability density estimation, Gaussian mixture distribution, and shadow detection [24].

The sealed steel wire rope of freight ropeway studied in this paper is applied to the outdoor environment. The collected surface images of steel wire rope are difficult to distinguish defects due to the following reasons: (1) the edge of the rope strand is blurred due to strong light, weak light, reflection, uneven illumination, and shadow, as shown in Figure 3a–c; (2) It is difficult to distinguish the edge of the rope strand due to oil stain on the surface, as shown in Figure 3d; (3) The surface texture of steel wire rope changes irregularly along the motion direction between adjacent image frames. The above characteristics make it difficult for the existing dynamic background modeling methods to effectively segment the strand on the surface of steel wire rope and detect fracture defects.

In order to solve the above problems, this paper proposes a steel wire rope surface defect detection method based on the segmentation template and the spatiotemporal gray sample set. The technical route of the method is shown in Figure 4. The steel wire rope segmentation template is constructed in advance, the rope strands of the real-time steel wire rope image are segmented and corrected by using the template, and the pixel information is extracted from continuous multi-frame images to build a dynamic pixel queue, combined with the rope strand information, the spatiotemporal gray sample set is constructed, the similarity between the real-time image and the spatiotemporal gray sample set is compared, and the defect area in the steel rope surface image is extracted and marked. The main contributions of this paper are as follows:(1)

Based on the geometric and texture features of the sealing wire rope, the segmentation template of the sealing wire rope is created for the first time;

The strand segmentation and correction method based on the steel wire rope segmentation template is proposed, which effectively solves the problem of strand segmentation caused by the variable speed movement and vibration of the ore hopper car;

The spatiotemporal gray sample set of pixel points in the relative motion environment between the camera and the scene is constructed for the first time and is used for wire rope defect detection, which effectively solves the problem of defect detection caused by light and oil pollution.

The remainder of this paper is organized as follows: a steel wire rope surface defect detection method based on steel wire rope segmentation template and spatiotemporal gray sample set is proposed in Section 2. Section 3 describes the content of algorithm implementation. In Section 4 and Section 5, the validity of the method is verified experimentally and the conclusions of this study are given.

Steel cables with loops 2mm thick. for securing small objects. There are loops at the ends. For fixing, for example: Paintings Parking cards Signs Garden images Laptops Flightca…

RF2BAR5HN–Old wire rope cable, block, pulley fixed to the the ground, used to winch fishing boats up the beach in the village of Cadgwith, Cornwall, England

RF2HB492T–Padlocks hanging on a wire rope. Love locks on netting fence at the brigde in New Wesminster BC. Street view, nobody, selective focus, blurred backgro

RMKWDWP7–Sample of Roebling"s wire rope used on the Brooklyn Bridge on display in Allegheny Portage Railroad National Historic site, Pennsylvania, USA.

RF2BAR5GG–Old wire rope cable, block, pulley fixed to the the ground, used to winch fishing boats up the beach in the village of Cadgwith, Cornwall, England

RF2HB48H5–Padlocks hanging on a wire rope. Love locks on netting fence at the brigde in New Wesminster BC. Street view, nobody, selective focus, blurred backgro

RM2G993D5–A pair of used, grey, polyester working gloves pinned on twisted steel wire rope by a blue plastic peg, closeup view with green foliage background.

RF2G4GART–Loops of metal cable close up on white background isolated, space for text. Texture of heavy duty steel cable, steel wire rope or steel sling. Use for

RF2BAR5H5–Old wire rope cable, block, pulley fixed to the the ground, used to winch fishing boats up the beach in the village of Cadgwith, Cornwall, England

RF2HMD522–Close - up of a hand rail along a difficult hiking trail in the Franconian Switzerland, Germany, made of steel wire rope with a wire rope clip mounted

RF2HMD51T–Close - up of a hand rail along a difficult hiking trail in the Franconian Switzerland, Germany, made of steel wire rope with a wire rope clip mounted

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Galvanized steel wire rope is constructed of galvanized wires which are dipped in the tank containing molten zinc to form a thick layer of zinc coating before the wire is drawing through dies. Then these galvanized wires are drawn to reduce diameter and increase tensile strength. At the same time, zinc coating bonding to steel wires is compressed to form a more excellent layer to protect the steel form corroding.

Galvanized steel wire rope is divided into two categories: cold galvanized steel wire rope and hot-dip galvanized steel wire rope. Hot-dip galvanizing and cold-dip galvanizing have this essential difference in the process. Hot-dip galvanizing relies on physical thermal diffusion to form a coating. First, iron-zinc compounds are formed, and then a pure zinc layer is formed on the surface of the iron-zinc compounds. The essential difference between hot-dip galvanized steel wire rope and cold-dip galvanized steel wire rope is that the zinc layer of hot-dip galvanized steel has better corrosion resistance due to the zinc-iron alloy layer than cold galvanized steel. The surface condition of the hot-dip galvanized steel wire rope is blackened because of the hot-dip galvanized iron alloy. The electroplated surface has no chemical reaction, so it is still the original color of zinc, which looks brighter. In general, the zinc layer of hot-dip galvanizing is thicker, and the corrosion resistance is better. The zinc layer of electro-galvanized zinc is thinner and has poorer corrosion resistance. (Unless the zinc layer of cold galvanizing also reaches the level of thick zinc layer)

Galvanized steel wire rope can be used in high-level construction, blockade, fence, clothesline, vehicle and ship binding, towing, strapping and other fields; used in shipping, offshore oil exploration, aircraft manipulation, marine fishing, trawling, fixed net, roll net and other fisheries .

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