Traditional Convolutional Layers Research Articles

Automated collection of facial temperatures in dairy cows via improved UNet

In cattle, facial temperatures captured by infrared thermography provide useful information from physiological aspects for researchers and local practitioners. Traditional temperature collection requires massive manual operations on relevant software. Therefore, this paper aimed to propose a tool for automated temperature collection from cattle facial landmarks (i.e., eyes, muzzle, nostrils, ears, and horns). An improved UNet was designed by replacing the traditional convolutional layers in the decoder with Ghost modules and adding Efficient Channel Attention (ECA) modules. The improved model was trained on our open-source cattle infrared image dataset. The results show that Ghost modules reduced computational complexity and ECA modules further improved segmentation performance. The improved UNet outperformed other comparable models on the testing set, with the highest mean Intersection of Union of 80.76% and a slightly slower but still good inference speed of 32.7 frames per second. Further agreement analysis reveals small to negligible differences between the temperatures obtained automatically in the areas of eyes and ears and the ground truth. Collectively, this study demonstrates the capacity of the proposed method for automated facial temperature collection in cattle infrared images. Further modelling and correction with data collected in more complex conditions are required before it can be integrated into on-farm monitoring of animal health and welfare.

A new domain adaption residual separable convolutional neural network model for cross-domain remaining useful life prediction

In order to realize the remaining useful life (RUL) prediction of mechanical equipment under different operating conditions, a domain adaption residual separable convolutional neural network (DRSCN) model is proposed in this paper. In the DRSCN model, instead of the traditional convolutional layer, a residual separable convolutional module is developed to improve the feature extraction ability of the model. Moreover, a multi-kernel maximum mean discrepancy metric function and an adversarial learning mechanism are embedded in the DRSCN model to enhance its ability to resist domain shifts, thus improving the cross-domain RUL prediction accuracy of the model. The effectiveness of the DRSCN model is verified on an aircraft engine dataset. The experimental results show that the proposed model can realize high-accuracy RUL prediction.

TFN: An interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis

Convolutional neural network (CNN) is widely used in fault diagnosis of mechanical systems due to its powerful feature extraction and classification capabilities. However, the CNN is a typical black-box model, and the mechanism of CNN’s decision-making is not clear, which limits its application in high-reliability-required fault diagnosis scenarios. To tackle this issue, we propose a novel interpretable neural network termed as time-frequency network (TFN), where the physically meaningful time-frequency transform (TFT) method is embedded into the traditional convolutional layer as a trainable preprocessing layer. This preprocessing layer named as time-frequency convolutional (TFconv) layer, is constrained by a well-designed kernel function to extract fault-related time-frequency information. It not only improves the diagnostic performance but also reveals the logical foundation of the CNN prediction in a frequency domain view. Different TFT methods correspond to different kernel functions of the TFconv layer. In this study, three typical TFT methods are considered to formulate the TFNs and their diagnostic effectiveness and interpretability are proved through three mechanical fault diagnosis experiments. Experimental results also show that the proposed TFconv layer has outstanding advantages in convergence speed and few-shot scenarios, and can be easily generalized to other CNNs with different depths to improve their diagnostic performances. The code of TFN is available on https://github.com/ChenQian0618/TFN.

An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

Damage detection of composite materials using modal parameters has limitations in terms of sensitivity to small or localized damage and limited accuracy in damage localization. To address this issue, an enhanced channel attention residual network (ECARNet) damage detection model for composite laminates is proposed. First, finite element analysis is used to obtain training samples, which are processed as two-dimensional data to take full advantage of the convolutional neural network. Then, the residual module uses a multilayer perceptron instead of the traditional convolutional layers to learn the correlation between channels to enhance the generalization ability of the model, and uses the tanh activation function to retain negative information. Finally, a channel focus mechanism is introduced to enable the network to learn key features adaptively. Experimental results on two datasets with different levels of damage demonstrate the superior detection performance of ECARNet, achieving average detection accuracies of 98.13% and 97.94% respectively. A comparison with other methods verifies the effectiveness and reliability of the proposed approach. Furthermore, the effectiveness of the new method is validated on real-world test data.

Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we employ a three-layer PAFPN structure and incorporate a detection head tailored for small-sized objects using large-scale feature maps, significantly enhancing the algorithm’s capability to detect small-sized targets. Furthermore, we integrate the sandwich-fusion module into each layer of the neck’s up–down branch. This fusion mechanism combines network features with low-level features, providing rich spatial information about the objects at different layer detection heads. We achieve this fusion using depthwise separable evolution, which balances parameter costs and a large receptive field. In the network backbone, we employ RepVGG modules as downsampling layers, enhancing the network’s ability to learn multi-scale features and outperforming traditional convolutional layers. The proposed Drone-YOLO methods have been evaluated in ablation experiments and compared with other state-of-the-art approaches on the VisDrone2019 dataset. The results demonstrate that our Drone-YOLO (large) outperforms other baseline methods in the accuracy of object detection. Compared to YOLOv8, our method achieves a significant improvement in mAP0.5 metrics, with a 13.4% increase on the VisDrone2019-test and a 17.40% increase on the VisDrone2019-val. Additionally, the parameter-efficient Drone-YOLO (tiny) with only 5.25 M parameters performs equivalently or better than the baseline method with 9.66M parameters on the dataset. These experiments validate the effectiveness of the Drone-YOLO methods in the task of object detection in drone imagery.

YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection.

Tropical Convolutional Neural Networks (TCNNs) Based Methods for Breast Cancer Diagnosis

One of the leading causes of mortality for women worldwide is breast cancer. The likelihood of breast cancer-related mortality can be decreased by early identification and rapid treatment. Machine learning-based predictive technologies provide ways to detect breast cancer earlier. Several analytical techniques, such as breast MRI, X-ray, thermography, mammography, ultrasound, etc., may be used to find it. Accuracy metrics are the most extensively used approach for performance evaluation, and the Tropical Convolutional Neural Networks (TCNNs) model for breast cancer detection is the most precise and popular model. The proposed approach was examined using the Kaggle Breast Cancer Datasets (KBCD). The data set is partitioned into training and testing. We suggest a new class of CNNs called Tropical Convolutional Neural Networks (TCNNs), which are based on tropical convolutions and replace the multiplications and additions in traditional convolutional layers with additions and min/max operations, respectively, in order to reduce the number of multiplications. The results of the review demonstrated that the Tropical Convolutional Neural Networks (TCNNs) is the most successful and popular model for detecting breast cancer, and that accuracy metrics is the most popular approach for evaluating performance. It is amazing how deep learning is being used to so many different real-world problems. Additionally, because tropical convolution operators are basically nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNNs. The Kaggle Breast Cancer Datasets (KBCD) findings demonstrate that TCNN can reach more expressive power than regular convolutional layers.

SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut

In the past few decades, data-driven deep learning analysis has been developed and successfully applied in many domains, such as spectral signal analysis. The convolutional layer was regarded as an efficient feature extractor, and the residual learning framework was used to train the deep neural network. In this paper, by replacing identity blocks in a residual network with proposed short-cut structures, we present a new residual learning framework coupled with multiscale convolutional layers, whose advantages are discussed from the perspective of gradient descent during backpropagation and information theory. This study proved that the proposed short-cut structure can pass the loss gradient from the last layer to minimize the vanishing gradient problem. The experiments also showed that the multiscale design of our network can extract features from spectral signals more efficiently than traditional convolutional layers. The proposed new residual framework was tested on four public datasets to prove the network’s efficiency. Compared to the traditional residual network, the proposed method can decrease RMSE by 36.9% on average and the R2 reached 0.9544 on average.

Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into three sub-categories of tubular adenoma, villous adenoma and villioustublar adenoma, subsequently the images were re-classified into six categories. In this paper, we proposed a novel convolutional neural network termed Polyp-DedNet for the four- and six-category classification tasks of colorectal polyps. Based on the existing classification network ResNet50, Polyp-DedNet adopted dilated convolution to retain more high-dimensional spatial information and an Efficient Channel Attention (ECA) module to improve the classification performance further. To eliminate gridding artifacts caused by dilated convolutions, traditional convolutional layers were used instead of the max pooling layer, and two convolutional layers with progressively decreasing dilation were added at the end of the network. Due to the inevitable imbalance of medical image data, a regularization method DropBlock and a Class-Balanced (CB) Loss were performed to prevent network overfitting. Furthermore, the 5-fold cross-validation was adopted to estimate the performance of Polyp-DedNet for the multi-classification task of colorectal polyps. Mean accuracies of the proposed Polyp-DedNet for the four- and six-category classifications of colorectal polyps were 89.91% ± 0.92% and 85.13% ± 1.10%, respectively. The metrics of precision, recall and F1-score were also improved by 1%~2% compared to the baseline ResNet50. The proposed Polyp-DedNet presented state-of-the-art performance for colorectal polyp classifying on white-light and NBI colonoscopy images, highlighting its considerable potential as an AI-assistant system for accurate colorectal polyp diagnosis in colonoscopy.

From Front to Rear: 3D Semantic Scene Completion Through Planar Convolution and Attention-Based Network

Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise voxel-wise semantics from the single-view incomplete input data, a crucial but highly challenging problem for scene understanding. Although SSC has seen significant progress due to the introduction of 2D semantic priors in recent years, the occluded parts, especially the rear-view of the scenes, are still poorly completed and segmented. To ameliorate this issue, we propose a novel deep learning framework for 3D SSC, named Planar Convolution and Attention-based Network (PCANet), to effectively extend high-precision predictions of the front-view surface to the rear-view occluded areas. Specifically, we decompose the traditional convolutional layer into three successive planar convolutions to form a Planar Convolution Residual (PCR) block, which maintains the planar features of the 3D scene. Afterward, the Planar Attention Module (PAM) is proposed to capture three different planar attentions and harvest the global context from the front surface to the rear occluded areas to improve the overall accuracy. Extensive experiments on the real NYU and NYUCAD datasets and the synthetic SUNCG-RGBD dataset demonstrate that our proposed framework can generate high-quality SSC results in both front and rear views and outperforms the state-of-the-art approaches trained in an end-to-end manner without additional data.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Multi-channel feature fusion networks with hard coordinate attention mechanism for maize disease identification under complex backgrounds

Maize leaf disease has a negative impact on food security. If maize leaf disease is not controlled in time, it can spread and affect corn quality and yield. Severe maize leaf disease may even result in no harvest at all. In the existing deep learning technology for maize leaf disease recognition, due to the complex growing environment of maize, it is easy to capture the complex background of maize in the image acquisition process, which brings difficulties to the recognition of maize leaf disease. Therefore, we propose a new network architecture, HCA-MFFNet, for maize leaf disease recognition in a complex background. In HCA-MFFNet, hard coordinated attention (HCA) assigned at different spatial scales was used to extract features from maize leaf disease images to reduce the influence of complex backgrounds. In order to retain the feature information of maize leaf disease in the sampling process to the maximum extent, a multi-feature fusion network that can extract the weight information in two spatial directions was constructed. The traditional convolutional layer was replaced by depthwise separable convolutional layer, reducing the number of parameters in the network. In order to verify the feasibility and effectiveness of this model in complex environment, we compare it with existing methods. The average recognition accuracy of the model is 97.75%, and the F1 value is 97.03%. The proposed method is better than the existing methods.

Yarn-dyed fabric defect detection based on an improved autoencoder with Fourier convolution

Compared with solid-colored fabrics, the textures in yarn-dyed fabric images are more complex, making the task of defect detection more challenging. To achieve efficient detection, this study proposes an automatic detection framework for dyed fabric defects. The proposed framework consists of a hardware system and a detection algorithm. For efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of light sources and a mirror was developed. In addition, a defect detection algorithm based on Fourier convolution and a convolutional autoencoder is proposed. Abandoning the common way of adding noise, this paper proposes to generate image pairs for training using a random masking method in the training phase. In the autoencoder, some traditional convolutional layers are replaced with Fourier convolutional layers. Ablation experiments verify the effectiveness of the mask generation method and Fourier convolution. Compared with other defect detection methods, the proposed method achieves the best performance, which verifies the superiority of the method. The maximum detection speed of the developed system can reach 41 meters per minute, which can meet real-time requirements.

Mirror invariant convolutional neural networks for image classification

Deep convolutional neural networks (DCNNs) have been developed rapidly and they perform well on both classification and object detection tasks. However, its strong performance makes people ignore to study the invariance of the DCNNs, such as mirror invariance. In fact, the ability of DCNNs in handling mirror-symmetrical images remains limited. In this paper, a mirror transformation convolutional layer is proposed, which transforms several feature maps to produce mirror-symmetrical feature maps based on the traditional convolutional layer. By combining with the mirror transformation convolutional layer, the DCNNs will have mirror invariance and the performance of neural networks can be improved on classification tasks. A dataset for driver's and passenger's seatbelt detection has been collected, which is used to verify the effectiveness of the proposed convolutional layer. In the experiments, one of the state-of-the-art DCNNs, GoogLeNet, is collaborated with the mirror transformation convolutional layer to form a mirror invariant networks (MINets). The experimental results show that the MINets can achieve better classification performance than the original GoogLeNet. MINets can also reduce the risk of over-fitting caused by applying data augmentations to the dataset.

The Shrank YoloV3-tiny for spinal fracture lesions detection

Purpose: at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods: in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results: the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion: the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection.

Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification.

Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model’s ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.

Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis.

Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.

Automatic surface defect segmentation for hot-rolled steel strip using depth-wise separable U-shape network

Accurate and efficient image segmentation can contribute to improving the recognition rate of surface defects for hot-rolled steel strips. However, due to its variances in shape, position, defect type and fuzzy boundary, surface defect segmentation is a challenging task. To address this issue, a depth-wise separable U-shape network (DSUNet) is proposed. In order to reduce the computation complexity and accelerate the segmentation performance, depth-wise separable convolution is employed to replace the traditional convolutional layer. In addition, a multi-scale module is proposed to extract multi-scale context and improve the segmentation accuracy. The experimental results indicate that the accuracy and dice of DSUNet reach 95.42% and 80.8%, respectively, and the DSUNet can segment 38.5 images per second, which suggests that the DSUNet can precisely segment surface defects for hot-rolled steel strip with high efficiency.

A Gas Classification Algorithm of Electronic Noses Based on Convolutional Spiking Neural Network

The electronic nose is a gas detection instrument using the bionic olfactory mechanism, which usually consists of the gas sensor array and the gas classification algorithm. Furthermore, the capability of the gas classification algorithm is critical to the reliable accuracy of gas recognition. The process of gas classification usually involves pattern recognition of multiple time-related gas sensor response curves. Traditional gas classification algorithms are mainly machine learning methods, such as PCA, LDA, ICA, SVM, KNN, etc. These algorithms are relatively cumbersome because we need to extract handcrafted features before using them. Deep learning also has applications in electronic nose gas classification algorithms[1-4] which improve the accuracy of classification result, but most of deep neural networks have complex structures and consume huge computing resources. The spiking neural network is the third generation of artificial neural network, and its spiking neuron model which is more bionic than previous artificial neurons can process spike sequence signals[5]. The spiking neural network model has a simple structure with higher computational efficiency, and takes up less computational resources. Moreover, its time attribute makes it more suitable for processing information about time series. In order to simultaneously take advantage of the efficient feature extraction of the convolutional neural network and the high computational efficiency of the spiking neural network, our team converted the convolutional neural network into a convolutional spiking neural network(CSNN) and applied it to gas classification. The activation function layer in the traditional convolutional layer was replaced with the spiking neuron layer which used the IF or LIF spiking neuron model to transform the continuous values passed by the convolutional later into discrete values so as to achieve the transmission of spikes between layers. The first convolutional spiking layer was used as a spiking encoder, so the spiking encoding method such as Gaussian encoding was not used. The spike-firing-frequency output by the last layer of neurons was calculated to obtain the classification result. The probability that a gas sample belonged to a certain class was proportional to the spike-firing-frequency of the corresponding neuron of the class. Our team built a convolutional spiking neural network model with 9 layers of convolutional spiking layer and 2 layers of fully connected spiking layer, and used the food spoilage gas dataset collected by us and open source gas mixtures dataset[6] to evaluate the capability of our model. With regard to the gas mixtures dataset, ethylene, methane, CO and their mixed state need to be classified. After training, CSNN achieved the test accuracy of 92.6%, and the other algorithms’ test accuracy were 92.9% of ResNet-18, 91.2% of one-dimensional deep convolutional neural network(1D-DCNN) and 88.5% of SVM. As for the food spoilage gas dataset, 30 types of spoiled meat, vegetables, fruits and their mixed state samples were measured. The first task was to classify the major categories of spoiled food, furtuer, the 30 types of spoiled food odor samples were going to be divided into 4 categories: fresh food, spoiled meat, spoiled vegetables and spoiled fruits. After training, CSNN achieved the test accuracy of 81.4% which had a certain accuracy improvement comparing with 80.6% of ResNet-18, 80.1% of 1D-DCNN and 77.3% of SVM. The second task was to classify the subcategories of spoiled fruits, that is, 8 classes of spoiled fruit odor samples should be classified. After training, CSNN could achieve high test accuracy of 90.7%, and the accuracy of other algorithms was 88.8% of ResNet-18, 87.1% of 1D-DCNN and 77.9% of PCA+ANN. The CSNN output of a spoiled watermelon sample is shown in Figure 1.In conclusion, CSNN had similar odor classification performance to ResNet-18, but the computing resources occupied by CSNN was only 1/5 of ResNet-18. This research shows that the spiking neural network has the advantages of high odor classification accuracy, great calculation efficiency and occupying few computing resources. It is suitable as a gas classification algorithm of electronic nose and for further development.

An improved efficient model for structure-aware lane detection of unmanned vehicles

Lane detection algorithms require extremely low computational costs as an important part of autonomous driving. Due to heavy backbone networks, algorithms based on pixel-wise segmentation is struggling to handle the problem of runtime consumption in the recognition of lanes. In this paper, a novel and practical methodology based on lightweight Segmentation Network is proposed, which aims to achieve accurate and efficient lane detection. Different with traditional convolutional layers, the proposed Shadow module can reduce the computational cost of the backbone network by performing linear transformations on intrinsic feature maps. Thus a lightweight backbone network Shadow-VGG-16 is built. After that, a tailored pyramid parsing module is introduced to collect different sub-domain features, which is composed of both a strip pool module based on Pyramid Scene Parsing Network (PSPNet) and a convolution attention module. Finally, a lane structural loss is proposed to explicitly model the lane structure and reduce the influence of noise, so that the pixels can fit the lane better. Extensive experimental results demonstrate that the performance of our method is significantly better than the state-of-the-art (SOTA) algorithms such as Pointlanenet and Line-CNN et al. 95.28% and 90.06% accuracy and 62.5 frames per second (fps) inference speed can be achieved on the CULane and Tusimple test dataset. Compared with the latest ERFNet, Line-CNN, SAD, F1 scores have respectively increased by 3.51%, 2.84%, and 3.82%. Meanwhile, the result from our dataset exceeds the top performances of the other by 8.6% with an 87.09 F1 score, which demonstrates the superiority of our method.