Feature Pyramid Research Articles

GFENet: a lightweight network for efficient detection of abnormal objects in transmission line corridors

Abstract Abnormal objects in transmission line corridors pose a grave hazard to the security of the power system. The intelligent edge monitoring system, driven by a lightweight model, offers a promising approach. However, due to the limitations of the lightweight networks in addressing various uncertainties of anomalous objects, effectively balancing the efficiency and accuracy of existing methods presents a significant challenge. Therefore, a lightweight network called GFENet is proposed, which is designed to effectively address missed and false detections from the fluctuation in similarity of inter-class features and operational characteristics, as well as the diversity in intra-class shapes and scales under complex conditions. Firstly, learnable and efficient channel attention is proposed. This mechanism utilizes hybrid pooling fusion and weighted learning adjustment strategy to expand the receptive field, thereby capturing the distinctive visual features of the object. Next, we introduce Feature Pyramid Network and Path Aggregation Network to facilitate multi-scale feature interactions. Then, an efficient dynamic head is proposed, which employs a keypoint offset strategy to achieve scale, spatial, and task awareness. This enhances the understanding of object structure and shape without increasing computational costs. Finally, the experimental results on self-built dataset demonstrate that the GFENet can virtually balance network lightweighting and accuracy, significantly enhancing the ability to detect foreign object intrusions in complex environments.

Multimodal medical image fusion based on interval gradients and convolutional neural networks

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of QEN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{EN}$$\\end{document}, QNIQE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{NIQE}$$\\end{document}, QSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SD}$$\\end{document}, QSSEQ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SSEQ}$$\\end{document} and QTMQI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{TMQI}$$\\end{document}.

Brain tumor image segmentation method using hybrid attention module and improved mask RCNN

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information.

End-to-End Implicit Object Pose Estimation.

To accurately estimate the 6D pose of objects, most methods employ a two-stage algorithm. While such two-stage algorithms achieve high accuracy, they are often slow. Additionally, many approaches utilize encoding-decoding to obtain the 6D pose, with many employing bilinear sampling for decoding. However, bilinear sampling tends to sacrifice the accuracy of precise features. In our research, we propose a novel solution that utilizes implicit representation as a bridge between discrete feature maps and continuous feature maps. We represent the feature map as a coordinate field, where each coordinate pair corresponds to a feature value. These feature values are then used to estimate feature maps of arbitrary scales, replacing upsampling for decoding. We apply the proposed implicit module to a bidirectional fusion feature pyramid network. Based on this implicit module, we propose three network branches: a class estimation branch, a bounding box estimation branch, and the final pose estimation branch. For this pose estimation branch, we propose a miniature dual-stream network, which estimates object surface features and complements the relationship between 2D and 3D. We represent the rotation component using the SVD (Singular Value Decomposition) representation method, resulting in a more accurate object pose. We achieved satisfactory experimental results on the widely used 6D pose estimation benchmark dataset Linemod. This innovative approach provides a more convenient solution for 6D object pose estimation.

Multi-scale receptive field grouped and split attention network for real-time detection of hazardous chemical gas leaks in infrared images

Abstract The petrochemical industry faces frequent hazardous gas leaks, which demand precise and timely detection to avert severe consequences. Existing computer vision approaches encounter challenges due to limitations in gas characteristics and scene features. To address these issues, we propose a multiscale receptive field grouped and split attention network, GAS-YOLO, that integrates infrared imaging technology. Within GAS-YOLO, we design a novel module, multi-scale receptive field grouped convolution (MRFGConv), to preserve fine-grained information, preventing detail loss and addressing spatial attention feature-sharing issues. An innovative split convolution attention (SCA) mechanism in the C2f module effectively couples multi-scale features, balancing performance and efficiency. Additionally, the asymptotic feature pyramid network (AFPN) facilitates the mutual interaction of information between non-adjacent levels, enabling advanced feature fusion. Using benchmark InfraGasLeakDataset, GAS-YOLO surpasses YOLOv8-n by 5.8% mAP50, with SCA outperforming state-of-the-art attention models. Experiment results validate the effectiveness and feasibility of our proposed approaches, providing valuable insights into hazardous chemical gas leak detection.

A Deformable and Multi-Scale Network with Self-Attentive Feature Fusion for SAR Ship Classification

The identification of ships in Synthetic Aperture Radar (SAR) imagery is critical for effective maritime surveillance. The advent of deep learning has significantly improved the accuracy of SAR ship classification and recognition. However, distinguishing features between different ship categories in SAR images remains a challenge, particularly as the number of categories increases. The key to achieving high recognition accuracy lies in effectively extracting and utilizing discriminative features. To address this, we propose DCN-MSFF-TR, a novel recognition model inspired by the Transformer encoder–decoder architecture. Our approach integrates a deformable convolutional module (DCN) within the backbone network to enhance feature extraction. Additionally, we introduce multi-scale self-attention processing from the Transformer into the feature hierarchy and fuse these representations at appropriate levels using a feature pyramid strategy. This enables each layer to leverage both its own information and synthesized features from other layers, enhancing feature representation. Extensive evaluations on the OpenSARShip-3-Complex and OpenSARShip-6-Complex datasets demonstrate the effectiveness of our method. DCN-MSFF-TR achieves average recognition accuracies of 78.1% and 66.7% on the three-class and six-class datasets, respectively, outperforming existing recognition models and showcasing its superior capability in accurately identifying ship categories in SAR images.

LSR-Det: A Lightweight Detector for Ship Detection in SAR Images Based on Oriented Bounding Box

Convolutional neural networks (CNNs) have significantly advanced in recent years in detecting arbitrary-oriented ships in synthetic aperture radar (SAR) images. However, challenges remain with multi-scale target detection and deployment on satellite-based platforms due to the extensive model parameters and high computational complexity. To address these issues, we propose a lightweight method for arbitrary-oriented ship detection in SAR images, named LSR-Det. Specifically, we introduce a lightweight backbone network based on contour guidance, which reduces the number of parameters while maintaining excellent feature extraction capability. Additionally, a lightweight adaptive feature pyramid network is designed to enhance the fusion capability of the ship features across different layers with a low computational cost by incorporating adaptive ship feature fusion modules between the feature layers. To efficiently utilize the fused features, a lightweight rotating detection head is designed, incorporating the idea of sharing the convolutional parameters, thereby improving the network’s ability to detect multi-scale ship targets. The experiments conducted on the SAR ship detection dataset (SSDD) and the rotating ship detection dataset (RSDD-SAR) demonstrate that LSR-Det achieves an average precision (AP50) of 98.5% and 97.2% with 3.21 G floating point operations (FLOPs) and 0.98 M parameters, respectively, outperforming the current popular SAR arbitrary-direction ship target detection methods.

Dense Feature Pyramid Deep Completion Network

Most current point cloud super-resolution reconstruction requires huge calculations and has low accuracy when facing large outdoor scenes; a Dense Feature Pyramid Network (DenseFPNet) is proposed for the feature-level fusion of images with low-resolution point clouds to generate higher-resolution point clouds, which can be utilized to solve the problem of the super-resolution reconstruction of 3D point clouds by turning it into a 2D depth map complementation problem, which can reduce the time and complexity of obtaining high-resolution point clouds only by LiDAR. The network first utilizes an image-guided feature extraction network based on RGBD-DenseNet as an encoder to extract multi-scale features, followed by an upsampling block as a decoder to gradually recover the size and details of the feature map. Additionally, the network connects the corresponding layers of the encoder and decoder through pyramid connections. Finally, experiments are conducted on the KITTI deep complementation dataset, and the network performs well in various metrics compared to other networks. It improves the RMSE by 17.71%, 16.60%, 7.11%, and 4.68% compared to the CSPD, Spade-RGBsD, Sparse-to-Dense, and GAENET.

A New Feature Pyramid Network With Bidirectional Jump Connection Network for Small Defect Detection on Solar PV Wafer

A New Feature Pyramid Network With Bidirectional Jump Connection Network for Small Defect Detection on Solar PV Wafer

Remote Sensing Building Damage Assessment Based on Machine Learning

Abstract After the occurrence of various types of disasters, including natural disasters and man-made damage, aid workers need accurate and timely data, such as the damage status of buildings, in order to take effective measures for rescue. So as to solve this problem, this paper researches and designs a building damage classification system based on machine learning. The damage assessment system consists of two network models (building extraction network and damage classification network). This article analyzes and designs the structure of each network model, and discusses the principles related to computer vision in machine learning. Buildings in satellite images are segmented through Siamese Convolutional Neural Network, the BottleNeck Module and Feature Pyramid Network are used in the damage classification assessment network to detect damage to buildings in sub-temporal remote sensing images. Subsequently, the model was trained and tested on different disaster events on the xBD dataset. The results show that the building damage detection system based on Siamese-CNN achieves good detection accuracy, and the system has the advantages of simple operation, good timeliness and low resource consumption, and can well meet the needs of disaster assessment.

SSFPN: Selective Spatial Feature Pyramid Network for Remote Sensing Object Detection

Abstract Object detection, particularly for oriented small objects, benefits greatly from the generation of multi-scale feature maps by the feature fusion module. Despite the success of series of Feature Pyramid Network models in general object detection, their application in remote sensing object detection has received limited attention. Small oriented objects require contextual information for detection, while various types of objects need different long-range context. In this paper, we propose an intuitive and simple fusion module to be added in Feature Pyramid Network called Selective Spatial Feature Pyramid Network (SSFPN) to address these challenges. SSFPN dynamically adjusts the spatial receptive field across multi-scale feature maps, enhancing the modeling of contextual variations among different objects in remote sensing scenarios. In extensive experiments, SSFPN has achieved competitive results, i.e., an improvement of 0.34% after adding SSFPN to the Oriented RCNN model. The codes are available at https://github.com/Atlantisming/SSFPN.

A maritime image dehazing algorithm based on improved VQGAN

ABSTRACT Maritime image dehazing faces challenges as existing methods struggle to handle different concentrations of sea fog, and the treatment of details in dehazed results is often suboptimal. This paper proposes a maritime image dehazing algorithm based on the VQGAN mode. Specifically, since VQGAN is capable of generating high-quality discrete codebooks, we input the haze-free maritime dataset into VQGAN for pre-training to obtain a high-quality codebook prior. During the vector quantisation stage,high-quality vectors from the discrete codebook undergo distance calculations with the upsampled vectors containing haze.This process replaces the vectors containing haze with the high-quality vectors. To address the issue of residual fog, the authors introducing the FAM-UNet encoder.To tackle distortion issues, the authors introduced the Pyramid, Cascading, and Deformable Convolution Feature Alignment Module (PCD) into the original model. The experimental results indicate that our model can effectively restore clear images, providing assistance for subsequent advanced computer vision tasks..

Enhanced precision in greenhouse tomato recognition and localization

The core challenge in realizing automatic tomato harvesting in greenhouse environments lies in the precise identification and localization of the fruits. This paper introduces a comprehensive approach based on an improved YOLOv5 detection algorithm and optimized binocular stereo vision technology. Firstly, by introducing the C3-Transformer Encoder (CTM) structure and Bidirectional Feature Pyramid Network (Bi-FPN), this study enhanced the model’s ability to recognize tomatoes, especially under complex backgrounds and occlusion conditions. After field testing, the mAP50 accuracy reached 97.1%, an increase of 1.2 percentage points, enhancing detection precision. In addition, the ZED binocular camera was used, and the census stereo matching algorithm was optimized, significantly reducing disparity errors, thereby improving the accuracy of depth information. This allows the model to accurately calculate the three-dimensional spatial position of tomatoes obscured by branches and leaves, greatly improving the efficiency of the harvesting robot. Through field debugging verification with the harvesting robot, the method proposed in this study has shown high accuracy and reliability in the recognition and localization of tomatoes in complex greenhouse environments.

Research on Lightweight Rice False Smut Disease Identification Method Based on Improved YOLOv8n Model

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security.

Real-Time Detection and Counting of Wheat Spikes Based on Improved YOLOv10

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model’s feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research.

Multi-Task Environmental Perception Methods for Autonomous Driving.

In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems.

Exploring Factors Affecting the Performance of Neural Network Algorithm for Detecting Clouds, Snow, and Lakes in Sentinel-2 Images

Detecting clouds, snow, and lakes in remote sensing images is vital due to their propensity to obscure underlying surface information and hinder data extraction. In this study, we utilize Sentinel-2 images to implement a two-stage random forest (RF) algorithm for image labeling and delve into the factors influencing neural network performance across six aspects: model architecture, encoder, learning rate adjustment strategy, loss function, input image size, and different band combinations. Our findings indicate the Feature Pyramid Network (FPN) achieved the highest MIoU of 87.14%. The multi-head self-attention mechanism was less effective compared to convolutional methods for feature extraction with small datasets. Incorporating residual connections into convolutional blocks notably enhanced performance. Additionally, employing false-color images (bands 12-3-2) yielded a 4.86% improvement in MIoU compared to true-color images (bands 4-3-2). Notably, variations in model architecture, encoder structure, and input band combination had a substantial impact on performance, with parameter variations resulting in MIoU differences exceeding 5%. These results provide a reference for high-precision segmentation of clouds, snow, and lakes and offer valuable insights for applying deep learning techniques to the high-precision extraction of information from remote sensing images, thereby advancing research in deep neural networks for semantic segmentation.

PCB plug-in solder joint defect detection method based on coordinated attention-guided information fusion

Printed Circuit Boards (PCBs) are the foundational component of electronic devices, and the detection of PCB defects is essential for ensuring the quality control of electronic products. Aiming at the problem that the existing PCB plug-in solder defect detection algorithms cannot meet the requirements of high precision, low false alarm rate, and high speed at the same time, this paper proposes a method based on spatial convolution pooling and information fusion. Firstly, on the basis of YOLOv3, an attention-guided pyramid structure is used to fuse context information, and multiple convolutions of different size are used to explore richer high-level semantic information; Secondly, a coordinated attention network structure is introduced to calibrate the fused pyramidal feature information, highlighting the important feature channels, and reducing the adverse impact of redundant parameters generated by feature fusion; Finally, the ASPP (Atrous Spatial Pyramid Pooling) structure is implemented in the original Darknet53 backbone feature extraction network to acquire multi-scale feature information of the detection targets. With these improvements, the average detection accuracy of the enhanced network has been elevated from 94.45 to 96.43%. This experiments shows that the improved network is more suitable for PCB plug-in solder defect detection applications.

Infrared Image Enhancement Method of Substation Equipment Based on Self-Attention Cycle Generative Adversarial Network (SA-CycleGAN)

During the acquisition of infrared images in substations, low-quality images with poor contrast, blurred details, and missing texture information frequently appear, which adversely affects subsequent advanced visual tasks. To address this issue, this paper proposes an infrared image enhancement algorithm for substation equipment based on a self-attention cycle generative adversarial network (SA-CycleGAN). The proposed algorithm incorporates a self-attention mechanism into the CycleGAN model’s transcoding network to improve the mapping ability of infrared image information, enhance image contrast, and reducing the number of model parameters. The addition of an efficient local attention mechanism (EAL) and a feature pyramid structure within the encoding network enhances the generator’s ability to extract features and texture information from small targets in infrared substation equipment images, effectively improving image details. In the discriminator part, the model’s performance is further enhanced by constructing a two-channel feature network. To accelerate the model’s convergence, the loss function of the original CycleGAN is optimized. Compared to several mainstream image enhancement algorithms, the proposed algorithm improves the quality of low-quality infrared images by an average of 10.91% in color degree, 18.89% in saturation, and 29.82% in feature similarity indices. Additionally, the number of parameters in the proposed algorithm is reduced by 37.89% compared to the original model. Finally, the effectiveness of the proposed method in improving recognition accuracy is validated by the Centernet target recognition algorithm.

An Efficient Detection of the Pitaya Growth Status Based on the YOLOv8n-CBN Model

The pitaya is a common fruit in southern China, but the growing environment of pitayas is complex, with a high density of foliage. This intricate natural environment is a significant contributing factor to misidentification and omission in the detection of the growing state of pitayas. In this paper, the growth states of pitayas are classified into three categories: flowering, immature, and mature. In order to reduce the misidentification and omission in the recognition process, we propose a detection model based on an improvement of the network structure of YOLOv8, namely YOLOv8n-CBN. The YOLOv8n-CBN model is based on the YOLOv8n network structure, with the incorporation of a CBAM attention mechanism module, a bidirectional feature pyramid network (BiFPN), and a C2PFN integration. Additionally, the C2F module has been replaced by a C2F_DCN module containing a deformable convolution (DCNv2). The experimental results demonstrate that YOLOv8n-CBN has enhanced the precision, recall, and mean average precision of the YOLOv8n model with an IoU threshold of 0.5. The model demonstrates a 91.1% accuracy, a 3.1% improvement over the original model, and an F1 score of 87.6%, a 3.4% enhancement over the original model. In comparison to YOLOv3-tiny, YOLOv5s, and YOLOv5m, which are highly effective target detection models, the mAP@0.50–0.95 of our proposed YOLOv8n-CBN is observed to be 10.1%, 5.0%, and 1.6% higher, respectively. This demonstrates that YOLOv8n-CBN is capable of more accurately identifying and detecting the growth status of pitaya in a natural environment.