Research on Multi-Scenario Fruit Detection and Classification Based on Improved YOLOv5

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

MRI image classification of brain tumors is critical for accurate and early diagnosis. New developments in deep learning have revealed that inserting attention mechanisms into convolutional neural networks can greatly improve classification performance. This work assesses the effectiveness of Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) integrated with the ResNet50 model for brain tumor classification. The SE module recalibrates channel-wise feature responses, while CBAM enhances feature representation by combining channel and spatial attention. The SE and CBAM are combined with ResNet50, greatly increasing classification accuracy, precision, and recall when compared to the basic model, according to experimental results on MRI datasets. The suggested model employs attention mechanisms to focus valuable information selectively and suppress irrelevant information. The experiments on publicly available brain tumor datasets display great improvements over basic CNN models, with precision, recall, accuracy, as well as F1 score at 0.9914, 0.9903, 0.9945, and 0.9908, respectively. From these results, the importance of attention mechanisms in deep learning models for medical imaging is highlighted, which suggests that SE and CBAM modules can be available as more dependable and effective instruments for brain tumor classification in clinical settings. Future studies should investigate transformer-based and hybrid attention techniques to enhance automated brain tumor categorization.

Retinal diseases are the leading causes of visual impairment, and early diagnosis is essential for treatment. Optical coherence tomography (OCT), a non-invasive imaging technique, provides high-resolution images for retinal disease classification; however, its complexity and the limitations of manual diagnosis require efficient automated classification methods. To enableearly clinical diagnosis, this paper proposes a Convolutional Block Attention Module (CBAM)- Automatic Mixing Precision (AMP) network (CANet) for automated classification of retinal images. The model categorizes diabetic macular oedema (DME), choroidal neovascularisation (CNV), drusen. and normal cases, which is built upon the ResNet-34 architecture. CBAM is introduced into the residual block to propose the CBAM-Block residual block embedded in the ResNet-34 network model, which combines the channel and spatial attention mechanism to enhance the lesion feature extraction capability. AMP is used to accelerate the training and combine with transfer learning to enhance the model generalization. Meanwhile, median filtering, normalization, dynamic thresholding to remove white edges and data enhancement are used to optimize data quality and alleviate the problem of category imbalance. Classification experiments were performed on the OCT-2017 dataset for the four categories and ablation experiments were performed to demonstrate their effectiveness. The total classification accuracy of the model reaches 0.9890, the AUC value of all categories is 1, where the recall of CNV reaches 1. CBAM, AMP, and transfer learning improve the classification accuracy by 0.9%, 1.6%, and 9.4%, respectively, and the ablation experiments likewise prove that the model remains highly robust to noisy data. The experimental results show that the CANet model significantly improves the OCT image classification performance through multi-module integration, which provides an efficient and reliable technical solution for the automated diagnosis of retinal diseases.

To reduce the chance of being infected by the COVID-19, wearing masks correctly when entering and leaving public places has become the most feasible and effective ways to prevent the spread of the virus. It is a concern to how to quickly and accurately detect whether a face is worn a mask correctly while reduce missed detection and false detection in practical applied scenarios. In this paper, an improved algorithm is proposed based on the YOLO-v4 algorithm. The attention mechanism module is added to the appropriate network level to enhance the key feature points of face wearing masks and suppress useless information. Apart from that, three attention mechanism modules are added to different layers of the YOLO-v4 network for ablation experiments, including CBAM (convolutional block attention module), SENet (squeeze-and-excitation networks) and CANet (coordinate attention networks). The path-aggregation network and feature pyramid are used to extract features from images. Two network models were compared and improved in the experiment, and it is found that adding the dual-channel attention mechanism CBAM before the three YOLO heads of YOLOv4 and in the neck network had better detection performance than the single channel attention mechanism SENet and the coordinated attention mechanism CANet. The experimental results show that when the attention module CBAM and the YOLO-v4 model are integrated, the accuracy of the selected MAFA + WIDER Face dataset reaches the highest value of 93.56%, which is 4.66% higher than that of the original YOLO-v4.

A dual-modal vision system for non-invasive real-time monitoring of broiler diarrhea under low-light conditions.

Brain-computer interfaces (BCI) allow for the brain to communicate with electronic devices. Concerning the BCI paradigms, motor imagery uses brain signals to decode an imagined movement. However, this is a hard task given the low signal-to-noise ratio. Usually, the main steps in BCI models are pre-processing, feature extraction, and classification. In recent years, Convolutional Neural Networks (CNNs) have been gaining relevance in several areas of science due to their feature extraction, translation invariance, and parameter sharing capa-bilities. Another, more recent way of feature extraction is using attention mechanisms, which are layers of neural networks based on human attention and have the ability to highlight important features. A variation of the attention mechanism is the Convolutional Block Attention Module, which combines the CNN structure with the attention mechanism. In this work, we propose a new model that joins the core architecture of EEGNet, a compact CNN widely used in the literature, with the Convolutional Block Attention Module and residual connections. The residual connections were introduced to lower data degradation throughout the model. The results highlight the residual connection's importance for the performance of the model. The proposed model obtained a kappa result 5.2% better than the EEGNet with a p-value less than 0.01 on BCI Competition IV dataset 2a, which is a well-known dataset for Motor Imagery. Furthermore, the proposal was better than EEGNet for most subjects and had the best-worst case.

As power grid structure becomes increasingly complex, the impact of the operation status of key substation equipment on the safe and stable operation of the power grid is gradually increasing. Aiming at the problems of dense targets and occlusion recognition difficulties in the complex background of substations, an improved you-only-look-once (YOLO) v4 insulator defect detection algorithm is proposed. A large number of insulator and defect datasets were collected from the Internet. We use MobileNet-v2 to replace the backbone network of YOLOv4, and in view of the problem that the accuracy of YOLOv4 target detection model is not high for small targets or dense targets, convolutional block attention module (CBAM) attention mechanism is added to the backbone network to enhance the feature extraction ability, and ultra-lightweight subspace attention module (ULSAM) is added to the detection head to learn the cross-channel information. Experiments based on InDataset show that the insulator defect detection algorithm based on deep learning proposed achieves a high detection accuracy and detection speed; the mean average precision of the improved YOLOv4 model is 87.48%, and the recall rate of insulator defects reaches 79.84%, which meets the requirements of robustness and accuracy of insulator defect detection in real scenarios.

Maritime vessel detection in nearshore waters is a fundamental capability for artificial intelligence (AI)-enabled maritime transportation systems, including coastal monitoring, traffic management, and digital maritime services. Although attention mechanisms are widely incorporated into YOLO-based detectors, their relative effectiveness in marine environments under strictly controlled experimental conditions remains insufficiently clarified. This study presents a systematic comparison of Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and CLIP-based semantic fusion within a unified YOLOv8n framework for binary discrimination between ships and fishing boats in cluttered coastal imagery. All model variants were trained under identical data partitions and optimization settings to isolate architectural effects. The experimental results show that CA achieves the highest localization robustness (mAP@0.5:0.95 = 0.6127) and substantially improves precision (+7.13% over baseline), while CBAM provides the most balanced performance with the highest F1-score. In contrast, CLIP-based semantic fusion consistently degrades detection reliability, indicating limitations of global vision–language representations in small-scale maritime datasets. Precision–Recall and F1 analyses further reveal architecture-specific confidence calibration behaviors relevant to deployment-sensitive maritime applications. The findings provide practical guidance for selecting attention mechanisms in AI-driven maritime perception systems and support reliable AI integration in marine science and engineering applications.

An improved forest fire detection algorithm based on the You Only Look Once v7 (YOLOv7) is proposed in this paper to address the poor accuracy and speed of traditional forest fire detection. In order to guide this model to better focus on the features of forest smoke and fire, this paper introduces the Convolutional Block Attention Module (CBAM) channel attention mechanism and spatial attention mechanism in the backbone network and the head of the YOLOv7 model. It uses the SCYLLA-IoU (SIoU) loss function to replace the Complete-IoU (CIoU) loss function in order to improve the regression accuracy and convergence speed. Experiments show that the improved YOLOv7 algorithm achieves 78.8%, 65.4%, and 70.8% accuracy, recall, and average accuracy, respectively. Compared with the original algorithm, the accuracy, recall, and average accuracy are improved by 4.7% ,1.6% and 5.3%. The algorithm reduces missed detections, improves the accuracy and practicality of forest fire detection, and has good robustness in different complex scenarios.

The primary focus of this research article is to advance convolutional neural networks (CNNs) through the incorporation of Squeeze and Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks, with an emphasis on recalibrating channel wise features. This approach highlights channel relationships, with the SE block seamlessly integrated into a pre-trained DenseNet CNN model to recalibrate channel wise feature responses by explicitly modeling channel interdependencies. The study demonstrates the construction of SENet and CBAM architectures with SE and CBAM blocks on pretrained DenseNet models using transfer learning, achieving exceptional generalization on a cataract image dataset. The proposed architectures utilize layers of compressed network blocks that can establish direct connections with preceding layers through adjustable weight modifications. Comprehensive evaluations indicate that the DenseSE and DenseCBAM models both achieve 99% testing accuracy and exhibit strong performance across various metrics. Notably, DenseNet169 with SE and CBAM blocks attained 99% precision, surpassing the baseline model’s 95% precision. Furthermore, the Grad CAM technique enhances cataract disease identification, with the DenseCBAM and DenseSE models generating detailed heatmaps that support healthcare professionals in making swift and accurate retinal diagnoses.

In the intelligent tomato-picking scenario, challenges such as insufficient accuracy in recognizing the growth pose of target tomatoes and inaccurate positioning of picking and grasping points have led to low efficiency in automated picking. To address these issues, this paper introduces an object detection optimization model based on Yolov8s, termed YOLOv8S-ECC. The model focuses on “Judging tomato pose by the spatial vector of the relative position between the calyx and the center point of the fruit,” aiming to enhance high-precision positioning of both the tomato calyx and fruit, thereby laying the groundwork for subsequent pose judgment and picking point positioning. We have integrated the ECA (Efficient Channel Attention) and Coordinate attention mechanisms into the Backbone network and introduced the CBAM (Convolutional Block Attention Module) attention mechanism into the Neck network. The combined effect of these attention mechanisms effectively overcomes the recognition challenges posed by the calyx’s color texture, which closely resembles the environment. This integration has also enhanced the model’s robustness in complex field environments. Test results indicate significant improvements: the accuracy rate, recall rate, and mAP@50 for detecting tomato fruits and calyces are 81.7% and 87.5%, 92.7% and 85.9%, and 89.7% and 91.3%, respectively, compared to the original model. By encapsulating the algorithm and integrating it with the picking robot, tests in a simulated environment (different lighting conditions and foliage occlusion situations) show picking success rates of 93.02%, with an average picking operation time of 14.2 ± 0.855 s, including an image recognition and processing time of 0.035 s. This research offers an effective technical solution for high-precision visual perception and pose judgment in fruit and vegetable picking robots, contributing to improved quality in tomato industry picking operations.

The non-maximum suppression (NMS) algorithm, which merges neighboring bounding boxes as the detection result, is widely utilized in object detection methods. However, the traditional NMS algorithm is not suitable for text detection in natural scenes, especially for long texts and dense texts. In this paper, we observe that the coordinates of candidate bounding boxes are presented in skew distribution. On this observation, an improved NMS algorithm called SD-NMS (Skew Distribution NMS) is designed. First, the mode and the median of the coordinate set are counted to filter out the redundant bounding boxes. Then the left bounding boxes are merged for the location of the text regions. The SD-NMS method improves the detection ability of the model without extra model training and can be easily embedded in text detection methods. The experimental results show that our method obtains F-measure more than other NMS methods in public data sets ICDAR2015 and MSRATD500.

BackgroundGrain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: ‘Bainong 307’, ‘Xinmai 26’, and ‘Jimai 336’, and it has achieved unprecedented predictive counting accuracy.MethodThe images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains.ResultsThe CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R2 of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R2 of 0.92, an MAE) of 1.15, and an MRE) of 2.09%.ConclusionsImage segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.

Wheat head identification and localization are crucial for field management and yield estimation. Traditional wheat head detection relies on labor-intensive manual counting. To address this issue, this study establishes a densely populated wheat head dataset and correspondingly proposes an efficient, rapid real-time model of CB-YOLO, which incorporates a Convolutional Block Attention Module (CBAM) mechanism (a sophisticated fusion of spatial attention mechanism and channel attention mechanism) so as to integrate channel and spatial features. Also, with an incorporated Bidirectional Feature Pyramid Network (BiFPN) to the neck, crowded wheat head detection was enhanced by establishing connections from the backbone to the bottom-up path. This enhancement surpasses the performance of the basic YOLOv5L model, achieving [Formula: see text] of 94.3% and [Formula: see text] of 53.9%. Among 10 classical detection models, CB-YOLO has a relatively small parameter size of 44[Formula: see text]Mb and high computing speed of 108[Formula: see text]G FLOPs. Additionally, ablation experiments demonstrate that compared to YOLOv5L without CBAM and BiFPN, the feature fusion module improves [Formula: see text] from 93.2% to 94.3%, indicating its effectiveness in enhancing detection performance. In conclusion, CB-YOLO exhibits outstanding identification performance, showing its feasibility in practical agricultural applications, and future work will focus on improving its localization ability.

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds.