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TopIn the field of autonomous driving, accurate lane detection is paramount for the development of safe and reliable systems. Recent research has leveraged various innovative approaches to tackle the complexities of precise lane detection.
Fan et al. (2019) introduced a novel spinning convolutional network approach, demonstrating significant improvements in lane boundary detection, especially under complex road scenarios. This method represents a shift towards leveraging computational efficiency and simplicity without compromising detection performance.
Further advancements in the area are highlighted by Ping et al. (2021), who proposed a vision-based lane departure warning framework incorporating data fusion techniques and fuzzy logic. This approach addresses the limitations of static warning thresholds in conventional systems, offering a more adaptive and context-aware solution.
The potential of deep learning in enhancing lane detection capabilities is showcased by J. Li et al. (2017), who employed deep neural networks for structural prediction and lane detection in traffic scenes. This framework signifies a major leap towards achieving higher accuracy and reliability in detecting lanes amid complex traffic dynamics. Moreover, Lee et al. (2015) emphasized using probabilistic modeling and tracking techniques through a cascade particle filter approach. This method enhances the robustness and accuracy of lane detection systems, especially in dynamic driving environments, underscoring the importance of probabilistic approaches in autonomous vehicle navigation.
The development and enhancement of educational systems through semantic web-based strategies have been a focal point of recent research. Hu et al. (2022) conducted a comprehensive evaluation and comparative analysis of these strategies, emphasizing their potential to revolutionize educational practices by facilitating more effective and tailored learning experiences.
In the domain of semantic-based image retrieval, significant advancements have been made to improve the precision and efficiency of search mechanisms. Nhi et al. (2022) introduced a model utilizing C-tree and neighbor graph methodologies, which notably enhanced the retrieval process by optimizing the semantic relationships between images.
The field of image retrieval has also been enriched by approaches that focus on visual saliency to guide complex image retrieval processes. Wang et al. (2020) leveraged visual cues that attract human attention to improve the relevancy of retrieved images in complex datasets.
Encryption and security in image processing have seen innovative applications, particularly in protecting multimedia content. Yu et al. (2018) proposed a robust image encryption scheme that combines the quaternion Fresnel transform, chaos theory, and computer-generated holography, offering a high-security level suitable for multimedia applications in smart.
Additionally, D. Li et al. (2019) developed a CNN-based image watermarking generation scenario that ensures the security of digital images. This method is particularly designed for smart city applications, where ensuring the authenticity and integrity of multimedia content is crucial.
Lastly, the detection of tampered images is critical for maintaining the reliability of digital media. Mishra et al. (2024) explored the efficacy of using error level analysis (ELA) combined with CNNs in detecting subtle manipulations in images, highlighting the potential application in consumer electronics and multimedia forensics.
TopProposed Approach
Our symmetric architecture for lane detection is constructed on a symmetric encoder-decoder framework, as represented in Figure 1.
Figure 1. Proposed approach architecture