Traffic violation detection in India using genetic algorithm

Bhat, Akhilalakshmi T; Anupama,; Akshatha,; Rao, Mahima S; Pai, Deepthi G.

doi:10.1016/j.gltp.2021.08.056

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…From the perspective of image data governance, the function of the entire cultural relics image retrieval system is designed [18][19][20]. The design covers four key modules: image management, overview of cultural relics, user management, and image retrieval and classification.…”

Section: Design Of the Digital Retrieval System Of The Art Museummentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

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Section: Design Of the Digital Retrieval System Of The Art Museummentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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“…Nilai threshold ini sangat mempengaruhi akurasi pendeteksian pelanggaran (Prasantha, 2020). Algoritma pendeteksian pelanggaran lampu lintas dikembangkan juga dengan menggunakan kecerdasan buatan dan deep learning maupun algoritma genetik untuk meningkatkan akurasi dan kecepatan pendeteksian (Bhat et al, 2021;Franklin & Mohana, 2020;Tonge et al, 2020;Uy et al, 2017). Selain pendeteksian pelanggaran lalu lintas dari lampu, deteksi pelanggaran lalu lintas dapat dilakukan juga pada pelanggaran pengendara sepeda motor yang tidak menggunakan helm.…”

Section: Pendahuluanunclassified

Deteksi Pelanggaran Lampu Lalu Lintas Berdasarkan Sensor Visual

Sugandi

Lifitri

2022

j. sains. teknologi.

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Peningkatan jumlah pengendara yang begitu pesat yang belum mampu di tangani oleh fasilitas jalan berefek pada masalah pelanggaran lalu lintas yang menimbulkan banyak korban. Untuk mengurangi masalah pelanggaran lalu lintas tersebut, dibutuhkan sistem pendeteksi pelanggaran yang dapat mempermudah petugas dalam memantau terjadinya pelanggaran di persimpangan lampu lalu lintas. Pada penelitian ini telah dikembangkan sistem deteksi pelanggaran pada simpang lampu lalu lintas. Sistem dirancang dengan menggunakan tiga tahapan yaitu deteksi lampu hijau, deteksi kendaraan dan deteksi jumlah pelanggaran. Pada tahap pertama yaitu deteksi lampu hijau digunakan filter warna HSL yang akan mendeteksi warna hijau dari lampu lalu lintas. Untuk mendeteksi kendaraan digunakan metode background substraction, morphology filer dan blob detection. Tahapan terakhir adalah mendeteksi jumlah pelanggaran yang ditentukan dengan penentuan garis virtual yang ditempatkan di atas zebra cross. Pada tahap ini, pelanggaran ditentukan oleh pengendara yang melewati garis virtual atau berada di atas garis virtual. Sistem diimplementasikan menggunakan video hasil rekaman di beberapa simpang lalu lintas. Sistem yang dikembangkan telah berhasil mendeteksi jumlah pelanggar yang menerobos lampu lalu lintas. Hasil pengujian menunjukkan bahwa sistem deteksi pelanggaran telah berhasil mendeteksi pelanggaran dengan tingkat kesalahan rata-rata sebesar 1,1%.

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“…The early machine learning methods include artificial neural network [6], support vector machine, empirical mode decomposition [7], decision tree and so on. After more than 10 years, it has developed into the current deep learning technology, such as stacked supervised auto-encoder [8][9], deep convolutional network [10], deep belief network [11], deep residual network [12], genetic algorithm [13] and so on. For complex signals, new algorithms such as shift invariant dictionary learning [14] and particle swarm optimization (PSO) neural network [15] are proposed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

H¹,

Wang²,

Wang³

et al. 2022

J. vibroeng.

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In multi-stage gear transmission system, the accident is often not caused by a single fault, but by coupling fault. The decoupling and identification of coupling faults has always been a hot topic. Through the analysis of frequency characteristics and transmission characteristics, this paper explored the correlation of coupling faults in multistage gear transmission system, and analyzed the coupling mechanism of faults. According to the fault test-bed, a 3D model was built to analyze the contact force and vibration characteristics of gears at all stages. STFT and waterfall diagram were used to analyze the coupling correlation characteristics of experimental signals under two single faults and coupling faults: fixed-axis gear crack and planetary gear wear. The contact force transfer function of all stages of gears to the box was built by using the system identification theory. The source of the fault characteristic frequency component was found through the transfer function, and the transmission characteristic of the fault was revealed. It can be seen from the results that the fault coupling is not the superposition of frequency characteristics. In the coupled signal, the frequency characteristic with the largest amplitude increase is not the fault gear itself, but the intermediate gear connected with them and affected by them. This phenomenon is often misled and misjudged in the analysis.

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Traffic violation detection in India using genetic algorithm

Cited by 11 publications

References 10 publications

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Deteksi Pelanggaran Lampu Lalu Lintas Berdasarkan Sensor Visual

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

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