Comparison of the Parametrically Optimized Implementation of Viola–Jones Object Detection Method and MTCNN

Egorov, Aleksej D.; Idiyatullin, Almaz; Zakirov, Artur D.

doi:10.1109/cts53513.2021.9562926

Cited by 2 publications

(1 citation statement)

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“…The representative algorithms are Viola-Jones, Histogram of Oriented Gradient (HOG), and DPM (Deformable Part Model). Viola-Jones [9] pioneered the use of integral image techniques to expedite Haar-like input feature calculations. AdaBoost algorithm is used for feature selection to identify key visual features.…”

Section: A Traditional Object Detection Algorithmsmentioning

confidence: 99%

Event-Based Weak Oil Impurity Detection

Li,

Wang,

Chen

et al. 2024

IEEE Access

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Machine vision-based methods are usually applied to detect impurities in transparent liquids like water or alcohols. Oils have higher viscosity and lower light transmittance, so traditional vision detection methods do not work well. A weak oil impurity detection algorithm was proposed using event stream data. In this method, the binary image of the oil was firstly captured using an event camera. To reduce the noise interference from the event camera, image filtering and morphological operations were applied. Then, image algebra operations were used to reduce the oil container's bottom pattern. Finally, impurity detection was performed through the YOLOv5 network. Three common edible oils serve as the experimental samples. Small flying insects, raw material fragments, metal fragments, hair strands, and tin beads of various sizes are selected as the weak impurities. Experiments were performed on a dataset containing 3000 sample images. To the best of our knowledge, existing algorithms can detect impurities with the minimum size of 0.4mm, and most of the experimental samples are transparent liquids. The proposed method can be applied to detect impurities in water and oils, and the detectable impurity size limit is 0.2mm.INDEX TERMS Impurity detection, dynamic vision, event camera.

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Section: A Traditional Object Detection Algorithmsmentioning

confidence: 99%