Thermal infrared image semantic segmentation for night-time driving scenes based on deep learning

Maheswari, B. Uma; Reeja, S. R.

doi:10.1007/s11042-023-15882-0

Cited by 3 publications

(2 citation statements)

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“…This interface facilitates easy sharing of code files and datasets across users, while also improving individual productivity and enabling effortless collaboration. Additionally, the method foresees future requirements by guaranteeing that acquired image data is easily accessible for further examination and training of models [23]. This combination is especially helpful in the Colab environment for managing big datasets and promoting collaborative.…”

Section: Discussionmentioning

confidence: 99%

Comparing hybrid models for recognising objects in thermal images at nighttime

Bandi,

Rajakumari

2024

IJEECS

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This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

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Section: Discussionmentioning

confidence: 99%

Comparing hybrid models for recognising objects in thermal images at nighttime

Bandi,

Rajakumari

2024

IJEECS

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“…Because thermal infrared information [43] is not affected by illumination changes and extreme weather, semantic segmentation using thermal images has attracted great attention. Maheswari et al [44] presented a top-down attention and gradient alignment-based graph neural network (AGAGNN) to discover the crucial semantic information. EdgeFormer was proposed by Wang et al [45] to promote the segmentation performance in electrical equipment's edges and interiors.…”

Section: Related Work 21 Single-modal Segmentationmentioning

confidence: 99%

Self-Enhanced Mixed Attention Network for Three-Modal Images Few-Shot Semantic Segmentation

Song,

Zhang,

Bao

et al. 2023

Sensors

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As an important computer vision technique, image segmentation has been widely used in various tasks. However, in some extreme cases, the insufficient illumination would result in a great impact on the performance of the model. So more and more fully supervised methods use multi-modal images as their input. The dense annotated large datasets are difficult to obtain, but the few-shot methods still can have satisfactory results with few pixel-annotated samples. Therefore, we propose the Visible-Depth-Thermal (three-modal) images few-shot semantic segmentation method. It utilizes the homogeneous information of three-modal images and the complementary information of different modal images, which can improve the performance of few-shot segmentation tasks. We constructed a novel indoor dataset VDT-2048-5i for the three-modal images few-shot semantic segmentation task. We also proposed a Self-Enhanced Mixed Attention Network (SEMANet), which consists of a Self-Enhanced module (SE) and a Mixed Attention module (MA). The SE module amplifies the difference between the different kinds of features and strengthens the weak connection for the foreground features. The MA module fuses the three-modal feature to obtain a better feature. Compared with the most advanced methods before, our model improves mIoU by 3.8% and 3.3% in 1-shot and 5-shot settings, respectively, which achieves state-of-the-art performance. In the future, we will solve failure cases by obtaining more discriminative and robust feature representations, and explore achieving high performance with fewer parameters and computational costs.

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