LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Lane detection based on semantic segmentation can achieve high accuracy, but, in recent years, it does not have a mobile-friendly cost, which is caused by the complex iteration and costly convolutions in convolutional neural networks (CNNs) and state-of-the-art (SOTA) models based on CNNs, such as spatial CNNs (SCNNs). Although the SCNN has shown its capacity to capture the spatial relationships of pixels across rows and columns of an image, the computational cost and memory requirement needed cannot be afforded with mobile lane detection. Inspired by the channel attention and self-attention machine, we propose an integrated coordinate attention (ICA) module to capture the spatial-wise relationships of pixels. Furthermore, due to the lack of enhancement in the channel dimension, we created an efficient network with a channel-enhanced coordinate attention block named CCA, composed of ICA and other channel attention modules, for all-dimension feature enhancement. As a result, by replacing many repeated or iterative convolutions with the attention mechanism, CCA reduces the computational complexity. Thus, our method achieves a balance of accuracy and speed and has better performance on two lane datasets—TuSimple and ILane. At less than a few tenths of the computational cost, our CCA achieves superior accuracy compared to the SCNN. These results show that the low cost and great performance of our design enable the use of the lane detection task in autopilot scenarios.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

The advancement of autonomous driving systems hinges on accurate and reliable vehicle and lane detection. This paper presents an integrated method to improve autonomous driving systems by merging Histogram of Oriented Gradients (HOG)-based vehicle detection with Convolutional Neural Network (CNN)-based lane detection. HOG effectively identifies vehicles by capturing edge orientations and structural features, while CNNs excel in detecting intricate lane patterns through deep learning. The combination of these techniques offers a robust solution for detecting both vehicles and lanes, essential for autonomous navigation. Evaluated across a diverse dataset featuring various driving conditions, the system's performance is measured using precision, recall, F1 score (for vehicle detection), and accuracy (for lane detection). The results indicate significant enhancements in detection capabilities, leading to improved situational awareness and safer navigation. Future work will aim to refine the system further and tackle challenges in more complex driving environments, marking this approach as a promising advancement in autonomous driving technology.

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models.

This paper presents a novel approach for lane detection using a Convolutional Neural Network with Line Detection (CNN-LD) methodology, aimed at enhancing the accuracy and efficiency of road lane recognition. The proposed model leverages advanced pre-processing techniques, including distortion correction, color space transformation, and noise reduction, to prepare input images for effective feature extraction. The methodology incorporates edge detection using Sobel filters and the Hough transform for precise lane identification. A comprehensive dataset of 4,000 annotated images captured under diverse lighting conditions—daytime, low light, and night-time—was utilized to train and evaluate the model. The CNN-LD framework demonstrated superior performance, achieving an accuracy of 98.92% and an F1-Score of 97.90%, significantly outperforming traditional methods. The integration of the Kanade–Lucas–Tomasi (KLT) tracker ensures robust lane tracking, even in challenging environments. Experimental results indicate that the proposed approach effectively addresses common issues in lane detection, such as variations in visibility and road conditions. This research contributes to the development of intelligent transportation systems, providing a reliable solution for autonomous driving applications. Future work will focus on improving model robustness against adverse weather conditions and integrating multimodal data for enhanced lane detection capabilities.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.&lt;/div&gt;&lt;/div&gt;

Summary Detecting fault from seismic images is really useful for interpretation of geologic structures and stratigraphic features. With the recent developments in deep learning, this study makes it possible for efficient seismic fault detection based on convolutional neural network. In this work, we propose a general segmentation network that works on real 3D seismic data. We propose a novel network (GCS-Net) which includes a global context block (GC) and channel attention module together with spatial attention module (CS) between the encoder and decoder, instead of the U-Net based convolutional neural network. The GC block is able to capture the long-range dependencies and the CS block is utilized to further integrate local features with their global dependencies adaptively. The proposed approach was trained on a synthetic seismic data and tested by the real data. Experimental results show that our method has better performance to some extent.

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrates associated features among all channel maps regardless of their scales. We finally sum the outputs of SAM and CAM to further improve feature representation. Extensive experiments on four challenging datasets show that DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters.

The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods.

Lane detection is one of the most fundamental problems in the rapidly developing field of autonomous vehicles. With the dramatic growth of deep learning in recent years, many models have achieved a high accuracy for this task. However, most existing deep-learning methods for lane detection face two main problems. First, most early studies usually follow a segmentation approach, which requires much post-processing to extract the necessary geometric information about the lane lines. Second, many models fail to reach real-time speed due to the high complexity of model architecture. To offer a solution to these problems, this paper proposes a lightweight convolutional neural network that requires only two small arrays for minimum post-processing, instead of segmentation maps for the task of lane detection. This proposed network utilizes a simple lane representation format for its output. The proposed model can achieve 93.53% accuracy on the TuSimple dataset. A hardware accelerator is proposed and implemented on the Virtex-7 VC707 FPGA platform to optimize processing time and power consumption. Several techniques, including data quantization to reduce data width down to 8-bit, exploring various loop-unrolling strategies for different convolution layers, and pipelined computation across layers, are optimized in the proposed hardware accelerator architecture. This implementation can process at 640 FPS while consuming only 10.309 W, equating to a computation throughput of 345.6 GOPS and energy efficiency of 33.52 GOPS/W.

As is known to all, there are thousands of people died in traffic accidents in the world every year and 80% of the traffic accidents are due to the drivers' inattention. In order to reduce the occurrence of car accidents, the design of Cognitive Vehicle Safety (CVS) system has attracted wide attention. As an important functional system of the CVS system, Lane Departure Warning system's function is to warn the driver when the vehicle changes the lane without the driver's intention. In this paper, we present a method of lane detection which based on Lane geometric model and image connected region process. The model of the lane is produced by the projection of camera, which makes the parallel lane lines in real world become intersect. The lines on the same side of lane have the same geometric feature that can be used to build lane line geometric model for detection. The lane line can be classified as different lane line models by the geometric feature of it. In this paper, due to the different features of lane line in different images, the operation and the classification of modeling performs at the same time. But before that, we go through some process to pre-process the image, such as the image connected region process, morphology tophat filter, etc. The combination of these modules can overcome the universal lane detection problems, such as the road in different light conditions. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

IntroductionThe cigar leaves moisture content (CLMC) is a critical parameter for controlling curing barn conditions. Along with the continuous advancement of deep learning (DL) technologies, convolutional neural networks (CNN) have provided a way of thinking for the non-destructive estimation of CLMC during the air-curing process. Nevertheless, relying merely on single-perspective imaging makes it difficult to comprehensively capture the complementary morphological features of the front and back sides of cigar leaves during the air-curing process.MethodsThis study constructed a dual-view image dataset covering the air-curing process, and proposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images. Firstly, the model utilizes two independent and parallel ResNet as its backbone structure to capture the heterogeneous features of dual-view images. Secondly, the Dual Efficient Channel Attention (DECA) module is introduced to dynamically adjust the channel attention weights of the features, thereby facilitating interaction between the two branches. Lastly, a Multi-scale convolutional feature fusion (MSCFF) module is designed for the deep fusion of features from the front and back images to aggregate multi-scale features for robust regression.ResultsOn five-fold cross-validation, CADFFNet attains R2 of 0.974±0.007 and mean absolute error (MAE) of 3.80±0.37%. On an independent cross-region, cross-variety testing set, it maintains strong generalization (R2=0.899, MAE=5.82%), compared with the classic CNN models ResNet18, GoogLeNet, VGG19Net, DenseNet121, and MobileNetV2, its R2 value has increased by 0.047, 0.041, 0.055, 0.098, and 0.090 respectively.DiscussionGenerally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.