The article puts forward a design of intelligent traffic control system based on traffic flow, and proposes the intersection video image processing and traffic flow detection algorithm. Application of DSP technology, design the system hardware, write the corresponding software, and realize the intelligent control of traffic light at the intersection according to traffic flow, improving the intersection vehicle capacity of passage.
The powerful conveyer belt is widely used in the mine, dock, and so on. After used for a long time, internal steel rope of the conveyor belt may fracture, rust, joints moving, and so on .This would bring potential safety problems. A kind of detection system based on x-ray is designed in this paper. Linear array detector (LDA) is used. LDA cost is low, response fast; technology mature .Output charge of LDA is transformed into differential voltage signal by amplifier. This kind of signal have great ability of anti-noise, is suitable for long-distance transmission. The processor is FPGA. A IP core control 4-channel A/D convertor, achieve parallel output data collection. Soft-core processor MicroBlaze which process tcp/ip protocol is embedded in FPGA. Sampling data are transferred to a computer via Ethernet. In order to improve the image quality, algorithm of getting rid of noise from the measurement result and taking gain normalization for pixel value is studied and designed. Experiments show that this system work well, can real-time online detect conveyor belt of width of 2.0m and speed of 5 m/s, does not affect the production. Image is clear, visual and can easily judge the situation of conveyor belt.
Vehicle flow detection is the key technology of intelligence traffic system. In order to overcome the shortcomings of traditional detection method which with low detection precision and is difficult to install and maintenance, a method, based on machine vision, which combines adjacent frame difference and background frame difference is proposed, besides, another method of vehicles count by vertical projection is also proposed. The simulation results show that the proposed vehicle detection algorithm not only can achieve high detection accuracy, but also is very simple and fast.
The complex environmental influences often complicate the detection of longitudinal tears in conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false detection rates. In this study, we propose a new depth learning method specifically designed for detecting longitudinal tears in conveyor belts. This method employs a linear Charge-Coupled Device (CCD) camera to capture images of the conveyor belt. These images are subsequently processed with a modified version of You Only Look Once (YOLO)v7 model to identify instances of longitudinal tearing. The modified YOLOv7 model features Efficient Intersection over Union (EIoU) loss function as a substitute for the original loss function. Furthermore, a Simple Parameter-Free Attention Module (SimAM) is introduced in the detection head to improve detection accuracy. In this method, we introduced the SimSPPFCSPC module as a new spatial pyramid pooling model. This module enhances detection speed while maintaining detection accuracy. Experiment results demonstrate the effectiveness of the proposed method, achieving an impressive precision of 94.6% and a detection speed of approximately 110 Frames Per Second (FPS). Such accuracy and speed meet the requirements for online detection of longitudinal tearing in belt conveyors.
The algorithm for selecting and stitching the joint images of the conveyer belt with steel ropes in X-ray imaging system is studied. Detecting the state of conveyer belt is important in production process such as coal mine. And selecting the joint images is a primary task for further processing, especially for the on-line nondestructive detection of product line. Moreover, in order to make the better visual effect and analyze the whole joint effectively, it is necessary to stitch the joint images containing the same joint information. Based on the character of the conveyer belt X-ray images, an algorithm using the gray level histogram statistics for selecting and stitching the conveyer belt joint images is put forward in this paper. The practical application shows that the correct selection rate of the joint image is 100%, and the whole joint image is stitched inextenso and correctly. This algorithm for joint detecting can greatly improve the performance of processing, especially for the on-line nondestructive detection of product line. The results of practical application show that the algorithm we proposed is effective.
The principle of X-ray nondestructive testing (NDT) is analyzed, and the general scheme of the X-ray nondestructive testing system is proposed. The hardware of the system is designed with Xilinx!?s VIRTEX-4 FPGA in whichPowerPC and MAC IP core are embedded, and its peripheral circuits. The network communication software based on TCP/IP protocol, which runs on the hardware platform, is programmed by loading LwIP to PowerPC in XilKernal system. On the basis of analysing image processing algorithm, the image processing software running on the PC is programmed. The NDT of high-speed conveyor belt with steel wire ropes and network transfer function are implemented. It is a strong real-time system with rapid scanning speed, high reliability and remotely nondestructive testing function. The nondestructive detector can be applied to the detection of product line in industry.
Multimodal biometric could overcome the drawbacks of single biometric by combining two or more biometric traits for personal identity verification. The theory and experiments of multimodal biometric were studied based on hand vein, iris and fingerprint. Simple Average and Weighting Average fusion algorithm, the classical information fusion methods, were analyzed and the constraint conditions for improving the recognition accuracy had been deduced. Biometric recognition experiments were performed finally to verify the theory deduction results. It is significant to future research on multimodal biometric and provides basis for developing multibiometric systems.
On-line damages detection of mine conveyor belt is developed by using machine vision technology. By analyzing the feature of images collected by the CCD camera for the Mine Conveyer Belt, an algorithm of JPEG encoding based on linear difference is proposed for compressing and transmission, which solves the problem of high efficiency and real-time transmission of image data. A method based on the parameter test is proposed in the algorithm for transmission error control. The algorithm can effectively reduce the amount of data transmission, and can ensure the real-time performance of the network, which has important application value in the detection system based on machine vision.
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