Deep Learning-Based Image Extraction

Krupa, K. S.; Y. C., Kiran; Kavana, S. R.; Gaganakumari, M.; Meghana, R.; Varshana, R.

doi:10.47852/bonviewaia2202326

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Milan et al [12] proposed MOT16 and improved Faster R-CNN for better results. Zhang et al [13] used an enhanced YOLOv3 to detect and track vehicles, using deep learning frameworks to extract object appearance features and perform nearest neighbor matching, similar to image matching [14]. The tracking algorithm DeepSORT [15] improved robustness by incorporating a recognition algorithm to extract appearance features, enabling a comparison between current and previously stored features.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism

Gao,

Li,

et al. 2024

Agriculture

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The automatic cultivation of corn has become a significant research focus, with precision equipment operation being a key aspect of smart agriculture’s advancement. This work explores the tracking process of corn, simulating the detection and approach phases while addressing three major challenges in multiple object tracking: severe occlusion, dense object presence, and varying viewing angles. To effectively simulate these challenging conditions, a multiple object tracking dataset using simulated corn was created. To enhance accuracy and stability in corn tracking, an optimization algorithm, YOLOv8MS, is proposed based on YOLOv8. Multi-layer Fusion Diffusion Network (MFDN) is proposed for improved detection of objects of varying sizes, and the Separated and Enhancement Attention Module (SEAM) is introduced to tackle occlusion issues. Experimental results show that YOLOv8MS significantly enhances the detection accuracy, tracking accuracy and tracking stability, achieving a mean average precision (mAP) of 89.6% and a multiple object tracking accuracy (MOTA) of 92.5%, which are 1% and 6.1% improvements over the original YOLOv8, respectively. Furthermore, there was an average improvement of 4% in the identity stability indicator of tracking. This work provides essential technical support for precision agriculture in detecting and tracking corn.

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Section: Introductionmentioning

confidence: 99%

YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism

Gao,

Li,

et al. 2024

Agriculture

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People Detection Using Artificial Intelligence with Panchromatic Satellite Images

Golej,

Kukuliač,

Horák

et al. 2024

Applied Sciences

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The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R–CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm.

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Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

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Deep Learning-Based Image Extraction

Cited by 3 publications

References 6 publications

YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism

YOLOv8MS: Algorithm for Solving Difficulties in Multiple Object Tracking of Simulated Corn Combining Feature Fusion Network and Attention Mechanism

People Detection Using Artificial Intelligence with Panchromatic Satellite Images

Handwritten Recognition Techniques: A Comprehensive Review

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