Accurate Object Detection Research Articles

Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility.

Establishing effective learning bridge cross multi-scale feature maps for object detection and semantic segmentation

ABSTRACT In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed ‘Effective Learning Bridge’. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model’s backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model.

Automated Cloud Shadow Detection from Satellite Orthoimages with Uncorrected Cloud Relief Displacements

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun’s illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun’s illumination, and the second where both the sun’s illumination and the sensor’s viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor’s geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this.

Railway foreign object tracking and detection with spatial positioning and feature generalization enhancement

The existing deep learning foreign object tracking and detection algorithm is easily affected by complex environments and target occlusion, resulting in problems such as missed detection and low detection accuracy. A railway foreign object tracking and detection algorithm with spatial positioning and feature generalization enhancement is proposed. First, a multi-scale cascade improved GhostNet feature network is proposed to improve the feature extraction capability of infrared targets. Secondly, the spatial positioning and feature generalization enhancement module is designed by using the spatial position positioning and generalized morphological information of foreign objects to enhance the detection accuracy of targets with position movement and tracking trajectory changesin complex scenes. Then, a pyramid prediction network is constructed to obtain the detection anchor frame, type and confidence information of infrared railway foreign objects. Finally, by improving the DeepSORT tracking algorithm with improved category and confidence display, combined with Kalman filtering and Hungarian algorithm, railway foreign object tracking and detection in infrared weak light environment is realized. Experimental results show that the proposed method has a tracking and detection accuracy of railway foreign objects 83.3%, with an average detection rate of 11.3 frames per second. Compared with the comparison method, the proposed method has higher detection accuracy and has better performance in railway foreign object tracking and detection in infrared weak light scenes.

Modular YOLOv8 optimization for real-time UAV maritime rescue object detection.

The task of UAV-based maritime rescue object detection faces two significant challenges: accuracy and real-time performance. The YOLO series models, known for their streamlined and fast performance, offer promising solutions for this task. However, existing YOLO-based UAV maritime rescue object detection methods tend to prioritize high accuracy, often at the expense of real-time performance and ease of implementation and expansion. This study proposes a modular plug-and-play optimization approach based on the YOLOv8 framework, aiming to enhance real-time performance while maintaining high accuracy for UAV maritime rescue object detection. The proposed optimization modules are flexible, easy to implement, and extendable. In experiments on the large-scale publicly available SeaDronesSee dataset, our method achieved a 13.53% improvement in accuracy over YOLOv8x while reducing computational cost by 85.63%. Additionally, it surpassed the detection speed of the SeaDronesSee official code's two-stage detector by over 20 times, while maintaining comparable accuracy. Furthermore, our analysis of the experimental results highlights differences in detection difficulty among various objects and potential biases within the dataset.

On-Tree Mango Fruit Count Using Live Video- Split Image Dataset to Predict Better Yield at Pre-Harvesting Stage

This study introduces a method for fruit counting in agricultural settings using video capture and the YOLOv7 object detection model. By splitting captured videos into frames and strategically selecting representative frames, the approach aims to accurately estimate fruit counts while minimizing the risk of double counting. YOLOv7, known for its efficiency and accuracy in object detection, is employed to analyze selected frames and detect fruits on trees. Demonstrated the method's effectiveness through its ability to provide farmers with precise yield estimations, optimize resource management, and facilitate early detection of orchard issues such as pest infestations or nutrient deficiencies. This technological integration reduces labor costs and supports sustainable agricultural practices by improving productivity and decision-making capabilities. The scalability of the approach makes it suitable for diverse orchard sizes and types, offering a promising tool for enhancing agricultural efficiency and profitability. The researcher compared YOLOv5n, YOLOv5s, YOLOv7, and YOLOv7-tiny with eight-sided imaging techniques around the tree. The experimental results of YOLOv7 with the eight-sided technique performed best and achieved a count accuracy of 97.7% on a single tree in just 17.112 ms of average inference time. On multiple trees, it is 95.48% in just 17 ms of average inference time, with the help of an eight- sided method on tree images.

SMALL OBJECT DETECTION APPROACH BASED ON ENHANCED SINGLE-SHOT DETECTOR FOR DETECTION AND RECOGNITION OF INDONESIAN TRAFFIC SIGNS

The detection and recognition of traffic signs are crucial components of advanced driving assistance systems (ADAS) that enhance road safety. Current traffic sign detection and recognition model technology is proficient in identifying and interpreting traffic signs. However, for accurate detection and recognition, the traffic sign in the image must be of a certain minimum pixel size or distance from the driver's sight line for proper detection. The ADAS system should be capable of detecting and recognizing road traffic signs from a considerable distance as they come into the driver's line of vision. The higher the vehicle speed, the greater the distance required for the sign to be detected and recognized, allowing the driver sufficient time to react according to the sign's meaning. Addressing these challenges, this research proposes an enhanced version of the single shot detector (SSD) algorithm, commonly used in object detection, to improve the algorithm's ability to detect small objects. The proposed method involves adding an auxiliary layer module to the original SSD architecture to increase the feature map resolution and expand the conventional layer's receptive space. With the Enhanced SSD algorithm, the detection capability of the SSD can be significantly enhanced in terms of accuracy. The limitations of this study are related to the influence of occlusion and clutter, which might affect the performance of object detection, especially for small objects, which are more susceptible to being influenced by various factors. The research results demonstrate that Enhanced SSD improves object detection accuracy compared to the original SSD, with a mean average precision (mAP) of 97.87 compared to 95.35 for detecting 21 traffic signs in Indonesia.

CN-ViT- Visual Object Detection with VisionTransformer

Object detection has always been an important and challenging task in the field of computer vision. In recent years, Vision Transformers (ViT) have achieved remarkable results on image classification tasks, demonstrating its potential in vision tasks. In this paper, we propose CN-ViT, a novel Vision Transformer based visual object detection model.CN-ViT effectively improves the accuracy and robustness of object detection by combining the advantages of self-attention mechanism and convolutional neural network, and introducing the GCCA (Global Context Block and Coordinate Attention) module. In this paper, CN-ViT is evaluated on the Mini COCO standard dataset. The experimental results suggest that CN-ViT may outperform current mainstream object detection methods in terms of detection accuracy and speed. This study sheds light on the potential of Transformer architectures for complex visual tasks and offers valuable insights for future research in this area.

Seal Pipeline: Enhancing Dynamic Object Detection and Tracking for Autonomous Unmanned Surface Vehicles in Maritime Environments

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.

Enhancing detection accuracy of highly overlapping targets in agricultural imagery using IoA-SoftNMS algorithm across diverse image sizes

Object detection is crucial for agricultural monitoring and agriculture automation, yet conventional non-maximum suppression (NMS) algorithms often struggle in agricultural scenes with complex backgrounds and highly overlapping targets, especially in intersection over union (IoU) threshold setting and abnormal non-maximum removal. This study proposed the soft non-maximum suppression based on intersection over area (IoA-SoftNMS) algorithm, an advanced NMS algorithm designed to enhance detection accuracy in such challenging agricultural environments. IoA-SoftNMS employs intersection over area (IoA) to quantify overlap and integrate SoftNMS to boost overall detection precision and effectiveness. Validation experiments demonstrated that IoA-SoftNMS significantly increased detection accuracy, improving mean average precision (mAP) by 0.7 % to 3.5 % across various object detection models, without necessitating model retraining. Importantly, these enhancements barely affect the detection time. IoA-SoftNMS exhibited robust adaptability in detecting large images, especially when integrated with the slicing-aided hyper inference (SAHI) technique, where it notably improved detection accuracy during the slicing process. Application of IoA-SoftNMS elevates detection accuracy in medium (1000 × 3300 pixels) and large (2500 × 3400 pixels) images from 93 % to 96.9 % and 90.8 % to 95.3 %, respectively. These findings underscored the effectiveness of the proposed IoA-SoftNMS algorithm in improving object detection accuracy in agricultural settings and its potential to alleviate challenges posed by large image sizes, offering avenues for enhancing agricultural production precision and intelligence.

Image Enhancement Guided Object Detection in Visually Degraded Scenes.

Object detection accuracy degrades seriously in visually degraded scenes. A natural solution is to first enhance the degraded image and then perform object detection. However, it is suboptimal and does not necessarily lead to the improvement of object detection due to the separation of the image enhancement and object detection tasks. To solve this problem, we propose an image enhancement guided object detection method, which refines the detection network with an additional enhancement branch in an end-to-end way. Specifically, the enhancement branch and detection branch are organized in a parallel way, and a feature guided module is designed to connect the two branches, which optimizes the shallow feature of the input image in the detection branch to be as consistent as possible with that of the enhanced image. As the enhancement branch is frozen during training, such a design plays a role in using the features of enhanced images to guide the learning of object detection branch, so as to make the learned detection branch being aware of both image quality and object detection. When testing, the enhancement branch and feature guided module are removed, and so no additional computation cost is introduced for detection. Extensive experimental results, on underwater, hazy, and low-light object detection datasets, demonstrate that the proposed method can improve the detection performance of popular detection networks (YOLO v3, Faster R-CNN, DetectoRS) significantly in visually degraded scenes.

Shape-Guided Detection: A joint network combining object detection and underwater image enhancement together

Most of the existing underwater image object detection methods involve pre-processing, such as using underwater image enhancement, to improve the accuracy of object detection. However, pre-processing methods are designed to improve the subjective perception of the human eye, which does not necessarily improve the object detection performance and consumes a large amount of computational resources. Therefore, in this paper, we creatively combine these two tasks and propose a Shape-Guided Detection network (SGD) to simultaneously optimize underwater image enhancement and object detection. In the SGD network, we innovatively incorporate the prior shape features as a learnable module embedded in it to fully explore the shape characteristics and structural details of the target object. To ensure that the prior knowledge can be effectively fused into the global network structure, we design a Shape Prior Enhancement module, which aims to realize the deep integration of the prior information with the local details. In order to optimize the stability of model training and enhance its convergence performance, a dual strategy of explicit and implicit constraints is ingeniously proposed in our method. We conduct extensive experiments on public datasets and the results show that the combination of our method with different detectors significantly improves the performance. The object detection performance reaches up to 0.491 mAP for optical images and 0.576 mAP for sonar images, and improves the preprocessing speed by 0.1 s.

Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data

Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their number in various areas of the highway. This information is then processed to assess road congestion conditions, with the aim of detecting congestion. The data obtained were tested on two road scenarios and traffic conditions to evaluate the performance of the system. The results showed that the proposed system was able to detect congestion with an accuracy level of 59.2% from several experiments. The use of YOLOv8 enables real-time detection with efficient computing resources, making it a potential solution for large-scale deployment. This research shows that the incorporation of advanced object detection algorithms such as YOLOv8 with CCTV data can provide an effective solution for traffic management in large cities. This system is expected to improve response to congestion, help control traffic, and reduce the negative impact of congestion in urban areas

SOD-YOLOv8-Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes.

Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities. Despite the significant advancements in object detection, detecting small objects in images captured by high-altitude cameras remains challenging, due to factors such as object size, distance from the camera, varied shapes, and cluttered backgrounds. To address these challenges, we propose small object detection YOLOv8 (SOD-YOLOv8), a novel model specifically designed for scenarios involving numerous small objects. Inspired by efficient generalized feature pyramid networks (GFPNs), we enhance multi-path fusion within YOLOv8 to integrate features across different levels, preserving details from shallower layers and improving small object detection accuracy. Additionally, we introduce a fourth detection layer to effectively utilize high-resolution spatial information. The efficient multi-scale attention module (EMA) in the C2f-EMA module further enhances feature extraction by redistributing weights and prioritizing relevant features. We introduce powerful-IoU (PIoU) as a replacement for CIoU, focusing on moderate quality anchor boxes and adding a penalty based on differences between predicted and ground truth bounding box corners. This approach simplifies calculations, speeds up convergence, and enhances detection accuracy. SOD-YOLOv8 significantly improves small object detection, surpassing widely used models across various metrics, without substantially increasing the computational cost or latency compared to YOLOv8s. Specifically, it increased recall from 40.1% to 43.9%, precision from 51.2% to 53.9%, mAP0.5 from 40.6% to 45.1%, and mAP0.5:0.95 from 24% to 26.6%. Furthermore, experiments conducted in dynamic real-world traffic scenes illustrated SOD-YOLOv8's significant enhancements across diverse environmental conditions, highlighting its reliability and effective object detection capabilities in challenging scenarios.

ACDF-YOLO: Attentive and Cross-Differential Fusion Network for Multimodal Remote Sensing Object Detection

Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting conditions, and their detection accuracy can be greatly reduced. Infrared images often lack rich texture information, resulting in a high false-detection rate during target identification and classification. To address these challenges, we propose a novel multimodal fusion network detection model, named ACDF-YOLO, basedon the lightweight and efficient YOLOv5 structure, which aims to amalgamate synergistic data from both visible and infrared imagery, thereby enhancing the efficiency of target identification in remote sensing imagery. Firstly, a novel efficient shuffle attention module is designed to assist in extracting the features of various modalities. Secondly, deeper multimodal information fusion is achieved by introducing a new cross-modal difference module to fuse the features that have been acquired. Finally, we combine the two modules mentioned above in an effective manner to achieve ACDF. The ACDF not only enhances the characterization ability for the fused features but also further refines the capture and reinforcement of important channel features. Experimental validation was performed using several publicly available multimodal real-world and remote sensing datasets. Compared with other advanced unimodal and multimodal methods, ACDF-YOLO separately achieved a 95.87% and 78.10% mAP0.5 on the LLVIP and VEDAI datasets, demonstrating that the deep fusion of different modal information can effectively improve the accuracy of object detection.

Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital

The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.

CelloType: A Unified Model for Segmentation and Classification of Tissue Images.

Cell segmentation and classification are critical tasks in spatial omics data analysis. We introduce CelloType, an end-to-end model designed for cell segmentation and classification of biomedical microscopy images. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multi-task learning approach that connects the segmentation and classification tasks and simultaneously boost the performance of both tasks. CelloType leverages Transformer-based deep learning techniques for enhanced accuracy of object detection, segmentation, and classification. It outperforms existing segmentation methods using ground-truths from public databases. In terms of classification, CelloType outperforms a baseline model comprised of state-of-the-art methods for individual tasks. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multi-scale segmentation and classification of both cellular and non-cellular elements in a tissue. The enhanced accuracy and multi-task-learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

Deep Learning-Based Biomimetic Identification Method for Mask Wearing Standardization.

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters' number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized.