Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

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A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

Computational Intelligence and Neuroscience ◽

10.1155/2021/9218137 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Rui Wang ◽

Ziyue Wang ◽

Zhengwei Xu ◽

Chi Wang ◽

Qiang Li ◽

...

Keyword(s):

Object Detection ◽

Real Time ◽

High Speed ◽

Feature Fusion ◽

Autonomous Driving ◽

Detection Algorithm ◽

Model Parameters ◽

Detection Accuracy ◽

Time Operation ◽

On The Road

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

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A Novel Metal Foreign Object Detection for Wireless High-Power Transfer Using a Two-Layer Balanced Coil Array with a Serial-Resonance Maxwell Bridge

Electronics ◽

10.3390/electronics9122070 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2070

Author(s):

Sunhee Kim ◽

Haeyong Jung ◽

Youngjun Ju ◽

Yongseok Lim

Keyword(s):

Object Detection ◽

High Power ◽

Bridge Circuit ◽

Detection System ◽

Transmission Efficiency ◽

Foreign Object ◽

Power Transfer ◽

Coil Array ◽

Foreign Objects ◽

Change Detection Performance

In a wireless high-power transfer system with a distance of several tens of centimeters apart between the transmitter and receiver coils, one of the most challenging issues is to detect metallic foreign objects between the transmitter and receiver coils. The metallic foreign objects must be detected and removed since these reduce the transmission efficiency and cause heat generation of the transmitter and receiver. This paper presents two-layer symmetric balanced coil array so that if there are metallic foreign objects, it can be detected through the change of the inductance of the balanced coils. Since the balanced coil is composed of coils that are in a symmetrical relationship in position, there is no need for a reference coil, and interference between coils is reduced by dividing the coil into two layers. In addition, a novel serial-resonance Maxwell bridge circuit to improve the inductance change detection performance is presented in this paper. The proposed metallic foreign object detection system is implemented using two-layer balanced coil array with a serial-resonance Maxwell bridge and the experimental results show that voltage changes of hundreds of mV to several V occur when a metallic foreign object is inserted, so that even small metals such as clips can be detected.

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Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Journal of Imaging ◽

10.3390/jimaging7070104 ◽

2021 ◽

Vol 7 (7) ◽

pp. 104

Author(s):

Vladyslav Andriiashen ◽

Robert van Liere ◽

Tristan van Leeuwen ◽

Kees Joost Batenburg

Keyword(s):

Object Detection ◽

Meat Products ◽

Food Product ◽

Processing Technique ◽

Dual Energy ◽

Foreign Object ◽

Metal Debris ◽

X Ray ◽

Foreign Objects ◽

X Ray Imaging

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

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GPU Based Real-time Floating Object Detection System

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) ◽

10.2991/icence-16.2016.106 ◽

2016 ◽

Author(s):

Jie Yang ◽

Jian-min Meng

Keyword(s):

Object Detection ◽

Real Time ◽

Detection System ◽

Floating Object

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9E-04 Fracture analysis of human erythrocytes in accordance with colliding velocity using high-speed real-time imaging techniques

The Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME ◽

10.1299/jsmebio.2010.23.383 ◽

2011 ◽

Vol 2010.23 (0) ◽

pp. 383-384

Author(s):

Yuki AKIMOTO ◽

Takanobu YAGI ◽

Yuko SEKI ◽

Daisuke KIDA ◽

Mitsuo UMEZU

Keyword(s):

Real Time ◽

High Speed ◽

Imaging Techniques ◽

Fracture Analysis ◽

Human Erythrocytes ◽

Real Time Imaging

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Real Time Object Detection Canbe Embedded on Low Powered Devices

10.31219/osf.io/t3gdy ◽

2019 ◽

Author(s):

Jimut Bahan Pal

Keyword(s):

Neural Networks ◽

Object Detection ◽

Open Source ◽

Real Time ◽

Detection System ◽

Computing Power ◽

Convolution Neural Networks ◽

Real Challenge ◽

New Algorithms

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time objectdetection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.

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