Counting of cigarettes in cigarette packets using LabVIEW

Sarkar, Abahan; Dutta, Tamal; Roy, B. K.

doi:10.1109/iccsp.2014.6950120

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…The initial method to filter out these defects utilized capacitive or optical sensors in pockets connected to a rotating turret or conveyor belt to inspect the presence or absence of cigarettes or normal alignment [26]- [28]. However, it is difficult to accurately match the operating points of the sensors and instruments used in the inspection method; durability issues due to long-term use and detection errors due to surrounding foreign substances are frequent, making management difficult.…”

Section: B Cigarette Detection Methodsmentioning

confidence: 99%

YOLO Network Optimization With a Single Circular Bounding Box for Detecting Defective Cigarettes

Park,

Park

2023

IEEE Access

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The manufacturing industry utilizes computing technology, robot technology, artificial intelligence, and IoT to improve production processes and quality. In particular, object detection technology is used in various industrial fields, and object detection methods based on deep learning are attracting attention. The tobacco processing industry requires automated production facilities, and quality control for defects in product appearance is essential. Mainly because tobacco products are sold at high prices, poor appearance is a significant issue in terms of consumer complaints and processing costs. Therefore, accurate cigarette detection is essential. We propose a modified network structure based on the YOLOv4-Tiny network, and use it to build a network optimized for cigarette detection. The modified network uses a single circular bounding box for learning and fast detection. It utilizes visual techniques, such as gradient-weighted Class Activation Mapping (Grad-CAM) to analyze the degree of activation of the network to construct an optimal network. This reduces the size of the network and increases processing speed, while maintaining detection accuracy. This paper is expected to play an important role in quality control and efficient production in the manufacturing industry.INDEX TERMS Circular bounding box, detecting defective cigarettes, tobacco processing, You Only Look Once (YOLO).

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Section: B Cigarette Detection Methodsmentioning

confidence: 99%

YOLO Network Optimization With a Single Circular Bounding Box for Detecting Defective Cigarettes

Park,

Park

2023

IEEE Access

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“…A sorting method for apples is reported [12] using machine vision. Automatic counting of cigarettes in cigarette packets using LabVIEW is discussed [13]. Real time barcode detection method [14][15][16][17][18] and its implementation on chip [14] are reported.…”

Section: Introductionmentioning

confidence: 99%

Fault identification on cigarette packets - an image processing approach

Sarkar

Dutta

Roy

2014

2014 Annual IEEE India Conference (INDICON)

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The proposed work presents a technique for an automated process for fault diagnosis in cigarette packets. The proposed work is based on application of image processing techniques in LabVIEW platform. The objectives of the proposed work are (i) to count the number of cigarettes in an open packet, and (ii) to check for defects, if any, in the barcode. National Instrument's Smart camera is used to capture images of cigarette packets moving in a conveyor belt and process the images to fulfill the above objectives. The technique was subjected to offline and on-line testing on more than 50 cigarette packets from five different vendors. The results obtained are found to be acceptable in all cases to fulfill the objectives.

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