DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging Conditions

Amirkhani, Abdollah; Barshooi, Amir Hossein

doi:10.1109/tits.2022.3212921

Cited by 18 publications

(2 citation statements)

References 59 publications

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“…Feature fusion is then performed to combine both deep features and 98.9% accuracy is reported. Multi-Agent Systems (MAS) [84] is a part-based ensemble framework consisting of several CNNs. Upon detecting the headlight, upper grill, fog light and bumper using You Only Learn One Representation (YOLOR) [87], image processing techniques follow to further refine the Region of Interest (ROI) before using them to fit CNNs.…”

Section: ) Quantitative Analysismentioning

confidence: 99%

“…For instance, the baseline network pays attention to the headlights given the frontal view of Land Rover Range Rover. Although the design of the headlights is unique [84], focusing on them alone increases the complexity of the VMMR task. On the contrary, CFCANet enlarges the focused region to include the whole frontal region but the windscreen.…”

Section: ) Qualitative Analysismentioning

confidence: 99%

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Coarse-to-Fine Context Aggregation Network for Vehicle Make and Model Recognition

Tan,

Chuah,

Chow

et al. 2023

IEEE Access

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Vehicle Make and Model Recognition (VMMR) is one of the fundamental elements in Intelligent Transportation System (ITS) that becomes the enabler for plenty of downstream tasks. Most of the past studies advance the recognition performance by focusing on the top-level feature maps but this practice hinders the ability of the network to learn features. Although the top-level feature maps are rich in global context information, they do not incorporate the fine-scale details that are embedded within the low-level feature maps. In this work, we bridge the gap by proposing a Coarse-to-Fine Context Aggregation (CFCA) module which effectively integrates information from feature maps of various scales. In particular, the crossscale features are generated by first refining the scale-specific components independently and then fusing them in a nonlinear manner through convolution. The resultant multi-scale feature maps are highly discriminative, as they contain both local subtle details and global abstract information. This is proven when the proposed framework achieves astounding classification performance on five publicly available datasets i.e. web-nature Comprehensive Cars (CompCars), Stanford Cars, Car-FG3K, surveillance-nature CompCars and Mohsin-VMMR. Moreover, the neurons exhibit high feature responses on the discriminative vehicle parts, corresponding to the superior feature extraction ability of the CFCA module. The CFCA module is also highly generalizable to other networks as it elevates the performance of VGG16, Inceptionv3, ResNet50 and DenseNet169.

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Section: ) Quantitative Analysismentioning

confidence: 99%

Section: ) Qualitative Analysismentioning

confidence: 99%

Coarse-to-Fine Context Aggregation Network for Vehicle Make and Model Recognition

Tan,

Chuah,

Chow

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Improved Arabic image captioning model using feature concatenation with pre-trained word embedding

Elbedwehy

Medhat

2023

Neural Comput & Applic

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Automatic captioning of images contributes to identifying features of multimedia content and helps in the detection of interesting patterns, trends, and occurrences. English image captioning has recently made incredible progress; however, Arabic image captioning is still lagging. In the field of machine learning, Arabic image-caption generation is generally a very difficult problem. This paper presents a more accurate model for Arabic image captioning by using transformer models in both the encoder and decoder phases as feature extractors from images in the encoder phase and a pre-trained word embedding model in the decoder phase. The models are demonstrated, and all of them are implemented, trained, and tested on Arabic Flickr8k datasets. For the image feature extraction subsystem, we compared using three different individual vision models (SWIN, XCIT, and ConvNexT) with concatenation to get among them the most expressive extracted feature vector of the image, and for the caption generation lingual subsystem, which is tested by four different pre-trained language embedding models: (ARABERT, ARAELECTRA, MARBERTv2, and CamelBERT), to select from them the most accurate pre-trained language embedding model. Our experiments showed that building an Arabic image captioning system that uses a concatenation of the three transformer-based models ConvNexT combined with SWIN and XCIT as an image feature extractor, combined with the CamelBERT language embedding model produces the best results among the other combinations, having scores of 0.5980 with BLEU-1 and with ConvNexT combined with SWIN the araelectra language embedding model having a score of 0.1664 with BLEU-4 which are higher than the previously reported values of 0.443 and 0.157.

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DQN-based ethical decision-making for self-driving cars in unavoidable crashes: An applied ethical knob

Vakili,

Amirkhani,

Mashadi

2024

Expert Systems with Applications

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DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging Conditions

Cited by 18 publications

References 59 publications

Coarse-to-Fine Context Aggregation Network for Vehicle Make and Model Recognition

Coarse-to-Fine Context Aggregation Network for Vehicle Make and Model Recognition

Improved Arabic image captioning model using feature concatenation with pre-trained word embedding

DQN-based ethical decision-making for self-driving cars in unavoidable crashes: An applied ethical knob

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