One-dimensional Convolutional Neural Network Research Articles

Coal-gangue sound recognition using hybrid multi-branch CNN based on attention mechanism fusion in noisy environments

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

An End-to-End Underwater Acoustic Target Recognition Model Based on One-Dimensional Convolution and Transformer

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition.

Investigation of Bird Sound Transformer Modeling and Recognition

Birds play a pivotal role in ecosystem and biodiversity research, and accurate bird identification contributes to the monitoring of biodiversity, understanding of ecosystem functionality, and development of effective conservation strategies. Current methods for bird sound recognition often involve processing bird songs into various acoustic features or fusion features for identification, which can result in information loss and complicate the recognition process. At the same time, the recognition method based on raw bird audio has not received widespread attention. Therefore, this study proposes a bird sound recognition method that utilizes multiple one-dimensional convolutional neural networks to directly learn feature representations from raw audio data, simplifying the feature extraction process. We also apply positional embedding convolution and multiple Transformer modules to enhance feature processing and improve accuracy. Additionally, we introduce a trainable weight array to control the importance of each Transformer module for better generalization of the model. Experimental results demonstrate our model’s effectiveness, with an accuracy rate of 99.58% for the public dataset Birds_data, as well as 98.77% for the Birdsonund1 dataset, and 99.03% for the UrbanSound8K environment sound dataset.

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

Data augmentation using a 1D-CNN model with MFCC/MFMC features for speech emotion recognition

Speech emotion recognition (SER) is attractive in several domains, such as automated translation, call centres, intelligent healthcare, and human–computer interaction. Deep learning models for emotion identification need considerable labelled data, which is only sometimes available in the SER industry. A database needs enough speech samples, good features, and a better classifier to identify emotions efficiently. This study uses data augmentation to enhance the amount of input voice samples and address the data shortage issue. The database capacity increases by adding white noise to the speech signals by data augmentation. In this work, the Mel-frequency Cepstral Coefficient (MFCC) and Mel-frequency Magnitude Coefficient (MFMC) features, along with a one-dimensional convolutional neural network (1D-CNN), are used to classify speech emotions. The datasets utilized to estimate the model's enactment were AESDD, CAFE, EmoDB, IEMOCAP, and MESD. The data augmentation with the 1D-CNN (MFMC) model performed best, with an average accuracy of 99.2% for AESDD, 99.5% for CAFE, 97.5% for EmoDB, 92.4% for IEMOCAP and 96.9% for the MESD database. The proposed 1D-CNN (MFMC) with data augmentation outperforms the 1D-CNN (MFCC) without data augmentation in emotion recognition.

CGAformer: Multi-scale feature Transformer with MLP architecture for short-term photovoltaic power forecasting

Accurately predicting the output power of photovoltaic (PV) systems is an effective means to ensure the reliable and economical operation of grid-connected PV systems. Aiming at the characteristics of PV power generation such as strong volatility, high intermittency and obvious periodicity, a hybrid model named CGAformer based on One-Dimensional Convolutional Neural Networks (CNN1D), Global Additive Attention (GADAttention), and Auto-Correlation is proposed for short-term PV power generation prediction. The model uses CNN1D to extract local features and obtains global weights by improving the GADAttetion obtained by additive attention. Auto-Correlation integrates local features and global weights and identifies repeated patterns in the sequence to obtain highly coupled multi-scale features, and finally generates the final prediction results through Multilayer Perceptron (MLP). In order to verify the effectiveness of the model, this paper uses a historical dataset from a PV system located in Uluru, Australia for sufficient experiments. In the comparative experiments, The overall average RMSE and MAE of CGAformer are improved by 6.82% and 20.46% respectively compared with long short-term memory (LSTM). In addition, ablation experiments and seasonal analysis are used to verify the effectiveness of the model and its excellent generalization ability for different seasons.

Identification of complex system radar emitter source based on one-dimensional convolutional neural network

Abstract With the continuous innovation of radar technology, the signal modulation of radar emitter sources has become more complex, which makes their identification face more challenges. We propose a method based on a one-dimensional convolutional neural network for the identification of complex system radar emitter sources and conduct an experiment on eight radar emitter sources with similar parameters and complex inter-pulse modulation. The identification accuracy reaches 88.24%.

Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm

Due to wide distribution and low energy efficiency, the energy-saving in industrial robots (IRs) is attracting extensive attention. Accurate energy consumption (EC) models of IRs lay the foundation for energy-saving. However, most dynamic and electrical parameters of IRs are not disclosed by manufacturers, which leads to the invalidity of most model-based EC prediction methods. To bridge this gap, a mechanism-data hybrid-driven method is proposed to predict the EC of IRs in this paper. First, a joint torque prediction model integrating a hybrid-driven parameter identification is developed based on deep reinforcement learning (DRL). The framework for DRL-based parameter identification is constructed through tailored design of interfaces and training mechanisms, wherein the DRL agent can learn to identify the dynamic parameters from the trajectory database. And a deep neural network based on long short-term memory (LSTM) is proposed to predict the EC of IRs according to the joint torques and velocities. The nonlinear item, which is not modeled in the robot dynamic equation, are also encapsulated in the deep neural network with one-dimensional convolutional neural network (1D-CNN) layers to improve the prediction accuracy. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 industrial robot with different loads. The results demonstrate that the proposed method can predict EC with a mean absolute percentage error of less than 2% under a fixed load and less than 3% under loads not used for agent training.

Explainability of deep convolutional neural networks when it comes to NIR spectral data: A case study of starch content estimation in potato tubers

Explainable AI is gaining popularity as a way to understand the decision-making processes of neural networks and gain insight into their predictions. In this paper, Integrated Gradients (IG) was applied to assess the relevance of spectral features used by deep convolutional neural networks in predicting the starch content of potatoes. For this purpose, spectral information of 7651 tubers of 12 potato varieties was acquired using a NIR spectrometer in the spectral range of 940–1650 nm. This was followed by a reference measurement of starch content. Three one-dimensional deep convolutional neural networks i.e. VGG-19, InceptionV3, and SpectraNet-32 were developed using the Keras API. The deep networks outperformed traditional models in the starch content prediction, with SpectraNet-32 achieving the highest prediction accuracy (R2 = 0.84, RMSE = 1.41%, RPD = 2.46, and rRMSE = 9.88%). Further analysis of the neural networks by IG indicated that the predictions were generated based on starch relevant spectral bands. The results of this study demonstrated that the deep convolutional neural networks not only could accurately predict starch content in potatoes, but also provided certainty in the predictions.

Deep Learning-based Emotion Recognition Algorithms in Music Performance

In the realm of artificial intelligence and musicology, emotion recognition in music performance has emerged as a pivotal area of research. This paper introduces EmoTrackNet, an integrated deep learning framework that combines sparse attention networks, enhanced one-dimensional residual Convolutional Neural Networks (CNNs) with an improved Inception module, and Gate Recurrent Units (GRUs). The synergy of these technologies aims to decode complex emotional cues embedded in music. Our methodology revolves around leveraging the sparse attention network to efficiently process temporal sequences, thereby capturing the intricate dynamics of musical pieces. The incorporation of the 1D residual CNN with an upgraded Inception module facilitates the extraction of nuanced features from audio signals, encompassing a broad spectrum of musical tones and textures. The GRU component further refines the model’s capability to retain and process sequential information over longer timeframes, essential for understanding evolving emotional expressions in music. We evaluated EmoTrackNet on the Soundtrack dataset a comprehensive collection of music pieces annotated with emotional labels. The results demonstrated remarkable improvements in the accuracy of emotion recognition, outperforming existing models. This enhanced performance can be attributed to the integrated approach, which efficiently combines the strengths of each component, leading to a more robust and sensitive emotion detection system. EmoTrackNet’s novel architecture and promising results pave the way for new avenues in musicology, particularly in understanding and interpreting the emotional depth of musical performances. This framework not only contributes significantly to the field of music emotion recognition but also has potential applications in music therapy, entertainment, and interactive media where emotional engagement is key.

Generalized zero-sample industrial fault diagnosis with domain bias

Generalized zero-sample fault diagnosis (GZSFD) is a challenging task involving the diagnosis of all samples from both previously seen and unseen faults. However, the scarcity of unseen samples for training causes that existing methods are hindered by domain bias, where unseen faults are more likely to be misclassified as seen faults. In this article, an efficacious solution is proposed by constructing an unseen fault detector for test samples in GZSFD with domain bias, which utilizes the detected unseen-sample knowledge to enhance the diagnosis performance. Specifically, a ResNet-based one-dimensional convolutional neural network is designed for high-quality feature extraction. Also, a Kullback–Leibler divergence-based distribution threshold detector is constructed for the identification of test samples. Afterwards, test samples are detected and distinguished into seen or unseen classes. In detected unseen classes, a zero-sample fault diagnosis (ZSFD) problem is undertaken, while in detected seen classes, a sub-GZSFD problem is addressed. For ZSFD tasks, to leverage the unseen samples in the test set, a clustering-based scheme without a predefined cluster number is used for the detected unseen fault. For sub-GZSFD tasks, combined with classification results in the ZSFD task, two embedding strategies are proposed to further mitigate the domain bias. They learn a shared weight and the optimal weights of semantic attributes from the feature space to the semantic embedding space, respectively. Using the shared fine-grained semantic attribute descriptions as auxiliary information, the final fault category can be determined. Experimental results showcase that the proposed strategies effectively alleviate the domain bias in GZSFD tasks.

MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Arrhythmias, also known as irregular heartbeats, are important health problems that must be accurately identified to diagnose and treat cardiovascular disease. Within the scope of this study, a network for classifying arrhythmias, which are important in the diagnosis and treatment of cardiovascular diseases, was proposed by using one-dimensional convolutional neural network (1D CNN), one of the deep learning techniques. With the proposed 1D-CNN architecture, arrhythmia types and normal rhythm ECGs were subjected to a more detailed examination from general to specific according to urgency situations. In the classifications made, first of all, a binary classification was made and an evaluation was made as whether there was a life risk or not. In triple, quadruple and six-fold classification, the detection of arrhythmia status is detailed. More complex classifications have helped to define different types of arrhythmias in more detail. This study proposes a deep learning network for automatic identification and classification of arrhythmias and shows that different arrhythmia conditions can be diagnosed with a single network model by applying the proposed network structure to multi-class arrhythmia disorders.

Acoustic emission failure detection and localisation in historic fibrous plaster ceiling

Fibrous plaster (FP) is a composite material that has been used for suspended decorative ceilings in historical buildings since the late 19th century. Such ceilings are susceptible to failures, particularly in hard-to-access structural hangers (‘wads’). Due to a limited understanding of structural behaviour of FP ceilings, maintenance and reconstruction decisions are based on visual and tactile inspections. Such inspections are costly, intrusive and ultimately subjective. Non-destructive techniques are needed to detect and localise failures in FP ceilings. An Acoustic Emission system was recently developed to detect and localise damage sources in FP ceilings. The method processes data from AE sensor clusters with one-dimensional convolutional neural networks(1D-CNNs) trained to account for wave propagation anisotropy. This paper presents a case study conducted at the Institution of Civil Engineers (ICE) building in London (UK) to validate the method. The results confirm the ability of the method to localise failures under real site conditions in 450 mm-by-450 mm monitoring areas. The method may enable non-intrusive detection of failures from the underside of ceilings during periodic inspections. The possibility of conducting pre-training in laboratories and covering large monitoring areas up to 1500mm-by-1500mm is established and may facilitate practical applications in the future.

Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network

Influenced by factors such as temperature, aging, and overloading, rubber bearings may undergo rupture during prolonged usage, leading to severe consequences such as bearing failure and structural damage. To accurately assess the degree of rubber rupture damage, this study proposed a novel generative adversarial network (GAN)-enhanced Bayesian-optimized one-dimensional convolutional neural network (1DCNN) framework (GAN-BCNN). In the GAN-BCNN framework, GAN is used to enhance the proportion of damaged data in the database, while Bayesian optimization is utilized to optimize crucial hyperparameters in the 1DCNN network. The predictive results indicate that the proposed GAN-BCNN model accurately predicts the degree of rubber rupture damage, achieving high classification accuracies of 99.1, 95.5, 92.9, and 98.0% for the categories of no damage, slight damage, moderate damage, and severe damage, respectively. When compared to the linear discriminant analysis model and the BCNN model without GAN enhancement, the proposed GAN-BCNN model exhibits a significant improvement in predictive accuracy, with an increase in Accuracy values of 52.4 and 6.0%, respectively. These results indicate that the GAN-BCNN model holds promising practical applications, and the GAN algorithm contributes to improving the prediction accuracy of the model.

UAV Anomaly Detection Method Based on Convolutional Autoencoder and Support Vector Data Description with 0/1 Soft-Margin Loss

Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1 loss function, with the goal of enhancing the accuracy and robustness of anomaly detection. Since the 0/1 loss function is a bounded, non-convex, and non-continuous function, this paper proposes the Bregman ADMM algorithm to solve the L0/1-SVDD. Finally, the difference between the reconstructed and the actual value is employed to train the L0/1-SVDD, resulting in a hypersphere classifier that is capable of detecting UAV anomaly data. The experimental results using real flight data show that, compared with methods such as AE, LSTM, and LSTM-AE, the proposed method exhibits superior performance across five evaluation metrics.

Prediction of inlet gas volume fraction of rotary vane pump under variable operational conditions with 1DCNN-TL model utilizing vibration signals

The Rotary vane pump (RVP) exhibits promising potential in the lubricating oil system of aircraft engines. The entrainment of air into the lubricating oil flowing through the pump affects the suction and discharge flow rates of the RVP. Therefore, understanding the inlet gas volume fraction (IGVF) of the RVP is crucial to assess its capability to deliver sufficient lubricating oil for ensuring the safe operation of aircraft engines. This study presents a one-dimensional convolutional neural networks (1DCNN)-based transfer learning (TL) (1DCNN-TL) prediction model to predict IGVF for the RVP using experimental data from gas-liquid two-phase flow in the RVP. The model is pre-trained using the vibration signals under a specific differential pressure between the pump inlet and outlet to enable predictions of the IGVF under varying differential pressures. The 1DCNN serves as pre-trained model for analyzing the outcomes of different transfer tasks. The effect of different frozen layer numbers on training time and prediction accuracy of the neural network is explored. Subsequently, the influence of varied source domain data on prediction accuracy is investigated, revealing that for optimal TL results, stable condition data at 200 kPa should be preferred as the source domain. Then the 1DCNN-TL prediction model of IGVF of RVP under variable operational conditions is determined. The results indicate varying iteration convergence rates of the pre-trained model under a differential pressure of 200 kPa across different transfer tasks, with a final goodness of fit exceeding 0.958 after fine-tuning the model. The model demonstrates the high prediction accuracy and applicability, which can contribute to improving the reliability and safety of aircraft engines.

Deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy employed for rapid and accurate identification of bismuth brass

BackgroundOwing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable. ResultsThis work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named “segmented fs-LA-SIBS wavelength”. The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %. SignificanceTo the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed “segmented fs-LA-SIBS wavelength” method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.

Air-Writing Recognition Enabled by a Flexible Dual-Network Hydrogel-Based Sensor and Machine Learning.

Accurate air-writing recognition is pivotal for advancing state-of-the-art text recognizers, encryption tools, and biometric technologies. However, most existing air-writing recognition systems rely on image-based sensors to track hand and finger motion trajectories. Additionally, users' writing is often guided by delimiters and imaginary axes which restrict natural writing movements. Consequently, recognition accuracy falls short of optimal levels, hindering performance and usability for practical applications. Herein, we have developed an approach utilizing a one-dimensional convolutional neural network (1D-CNN) algorithm coupled with an ionic conductive flexible strain sensor based on a sodium chloride/sodium alginate/polyacrylamide (NaCl/SA/PAM) dual-network hydrogel for intelligent and accurate air-writing recognition. Taking advantage of the excellent characteristics of the hydrogel sensor, such as high stretchability, good tensile strength, high conductivity, strong adhesion, and high strain sensitivity, alongside the enhanced analytical ability of the 1D-CNN machine learning (ML) algorithm, we achieved a recognition accuracy of ∼96.3% for in-air handwritten characters of the English alphabets. Furthermore, comparative analysis against state-of-the-art methods, such as the widely used residual neural network (ResNet) algorithm, demonstrates the competitive performance of our integrated air-writing recognition system. The developed air-writing recognition system shows significant potential in advancing innovative systems for air-writing recognition and paving the way for exciting developments in human-machine interface (HMI) applications.

Research on variety identification of common bean seeds based on hyperspectral and deep learning

Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common bean seeds non-destructively. In this study, the average spectrum of 3078 hyperspectral images from 500 varieties was obtained after image segmentation and sensitive region extraction, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to achieve the equilibrium of the samples of various varieties. A one-dimensional convolutional neural network model (IResCNN) incorporating Inception module and residual structure was proposed to identify seed varieties, and Support Vector Machine (SVM), K-Nearest Neighbor (KNN), VGG19, AlexNet, ResNet50 were established to compare the identification effect. After analyzing the effects of multiple spectral preprocessing methods on the model, the study selected Savitzky-Golay smoothing correction (SG) for spectral preprocessing and extracted 66 characteristic wavelengths using Successive Projections Algorithm (SPA) as inputs to the discriminative model. Ultimately, the IResCNN model achieved the highest accuracy of 93.06 % on the test set, indicating that hyperspectral technology can accurately identify bean varieties, and the study provides a correct method of thinking for the non-destructive classification of multi-species small-sample bean varieties.