Floating Point Operations Research Articles

Robot Operating Systems–You Only Look Once Version 5–Fleet Efficient Multi-Scale Attention: An Improved You Only Look Once Version 5-Lite Object Detection Algorithm Based on Efficient Multi-Scale Attention and Bounding Box Regression Combined with Robot Operating Systems

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities.

Chili Pepper Object Detection Method Based on Improved YOLOv8n.

In response to the low accuracy and slow detection speed of chili recognition in natural environments, this study proposes a chili pepper object detection method based on the improved YOLOv8n. Evaluations were conducted among YOLOv5n, YOLOv6n, YOLOv7-tiny, YOLOv8n, YOLOv9, and YOLOv10 to select the optimal model. YOLOv8n was chosen as the baseline and improved as follows: (1) Replacing the YOLOv8 backbone with the improved HGNetV2 model to reduce floating-point operations and computational load during convolution. (2) Integrating the SEAM (spatially enhanced attention module) into the YOLOv8 detection head to enhance feature extraction capability under chili fruit occlusion. (3) Optimizing feature fusion using the dilated reparam block module in certain C2f (CSP bottleneck with two convolutions). (4) Substituting the traditional upsample operator with the CARAFE(content-aware reassembly of features) upsampling operator to further enhance network feature fusion capability and improve detection performance. On a custom-built chili dataset, the F0.5-score, mAP0.5, and mAP0.5:0.95 metrics improved by 1.98, 2, and 5.2 percentage points, respectively, over the original model, achieving 96.47%, 96.3%, and 79.4%. The improved model reduced parameter count and GFLOPs by 29.5% and 28.4% respectively, with a final model size of 4.6 MB. Thus, this method effectively enhances chili target detection, providing a technical foundation for intelligent chili harvesting processes.

FE-YOLO: A Lightweight Model for Construction Waste Detection Based on Improved YOLOv8 Model

Construction waste detection under complex scenarios poses significant challenges due to low detection accuracy, high computational complexity, and large parameter volume in existing models. These challenges are critical as accurate and efficient detection is essential for effective waste management in the construction industry, which is increasingly focused on sustainability and resource optimization. This paper aims to address the low accuracy of detection, high computational complexity, and large parameter volume in the models of construction waste detection under complex scenarios. For this purpose, an improved YOLOv8-based algorithm called FE-YOLO is proposed in this paper. This algorithm replaces the C2f module in the backbone with the Faster_C2f module and integrates the ECA attention mechanism into the bottleneck layer. Also, a custom multi-class construction waste dataset is created for evaluation. FE-YOLO achieves an mAP@50 of 92.7% on this dataset, up by 3% compared to YOLOv8n. Meanwhile, the parameter count and floating-point operations are scaled down by 12% and 13%, respectively. Finally, a test is conducted on a publicly available construction waste dataset. The test results demonstrate the excellent performance of this algorithm in generalization and robustness.

Lightweight Algorithm for Rail Fastener Status Detection Based on YOLOv8n

To improve the accuracy of rail fastener detection and deploy deep learning models on mobile platforms for fast real-time inference, this paper proposes a defect detection model for rail fasteners based on an improved YOLOv8n. Considering the significant aspect ratio differences of rail fasteners, we designed the EIOU+ as the regression box loss function. The model is compressed and trained using an improved channel-wise knowledge distillation (CWD+) approach to address the challenge of accurately recognizing minor defects in rail fasteners. We introduced a feature extraction module to design a feature extraction network as the distillation teacher model (YOLOv8n-T) and a lightweight cross-stage partial bottleneck with two convolutions and a fusion module (C2f) to improve the YOLOv8n backbone network as the distillation student model (YOLOv8n-S). Experiments conducted on data collected from actual rail lines demonstrate that after CWD+ distillation training, the model’s mean detection accuracy (IOU = 0.5) reached 96.3%, an improvement of 2.7% over the original YOLOv8n algorithm. The recall rate increased by 4.5%, the precision by 2.7%, the number of floating-point operations decreased by 13%, and the detection frame rate frames per second (FPS) increased by 6.1 frames per second. Compared with other one-stage object detection algorithms, the CWD+ distilled model achieves the precise real-time detection of rail fastener conditions.

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks

Complex-valued convolutional neural networks (CVCNNs) have been demonstrated effectiveness in classifying complex signals and synthetic aperture radar (SAR) images. However, due to the introduction of complex-valued parameters, CVCNNs tend to become redundant with heavy floating-point operations. Model sparsity is emerged as an efficient method of removing the redundancy without much loss of performance. Currently, there are few studies on the sparsity problem of CVCNNs. Therefore, a complex-valued soft-log threshold reweighting (CV-SLTR) algorithm is proposed for the design of sparse CVCNN to reduce the number of weight parameters and simplify the structure of CVCNN. On one hand, considering the difference between complex and real numbers, we redefine and derive the complex-valued log-sum threshold method. On the other hand, by considering the distinctive characteristics of complex-valued convolutional (CConv) layers and complex-valued fully connected (CFC) layers of CVCNNs, the complex-valued soft and log-sum threshold methods are respectively developed to prune the weights of different layers during the forward propagation, and the sparsity thresholds are optimized during the backward propagation by inducing a sparsity budget. Furthermore, different optimizers can be integrated with CV-SLTR. When stochastic gradient descent (SGD) is used, the convergence of CV-SLTR is proved if Lipschitzian continuity is satisfied. Experiments on the RadioML 2016.10A and S1SLC-CVDL datasets show that the proposed algorithm is efficient for the sparsity of CVCNNs. It is worth noting that the proposed algorithm has fast sparsity speed while maintaining high classification accuracy. These demonstrate the feasibility and potential of the CV-SLTR algorithm.

Knowledge Distillation for Enhanced Age and Gender Prediction Accuracy

In recent years, the ability to accurately predict age and gender from facial images has gained significant traction across various fields such as personalized marketing, human–computer interaction, and security surveillance. However, the high computational cost of the current models limits their practicality for real-time applications on resource-constrained devices. This study addressed this challenge by leveraging knowledge distillation to develop lightweight age and gender prediction models that maintain a high accuracy. We propose a knowledge distillation method using teacher bounds for the efficient learning of small models for age and gender. This method allows the student model to selectively receive the teacher model’s knowledge, preventing it from unconditionally learning from the teacher in challenging age/gender prediction tasks involving factors like illusions and makeup. Our experiments used MobileNetV3 and EfficientFormer as the student models and Vision Outlooker (VOLO)-D1 as the teacher model, resulting in substantial efficiency improvements. MobileNetV3-Small, one of the student models we experimented with, achieved a 94.27% reduction in parameters and a 99.17% reduction in Giga Floating Point Operations per Second (GFLOPs). Furthermore, the distilled MobileNetV3-Small model improved gender prediction accuracy from 88.11% to 90.78%. Our findings confirm that knowledge distillation can effectively enhance model performance across diverse demographic groups while ensuring efficiency for deployment on embedded devices. This research advances the development of practical, high-performance AI applications in resource-limited environments.

Recognition of dispersed organic matter macerals using YOLOv5m model with convolutional block attention module

The identification of dispersed organic matter (DOM) macerals plays a crucial role in petroleum geology. However, the conventional manual statistical methods are both time-consuming and laborious. With advancements in deep learning, automatic computerized identification of macerals has become the prevailing trend. In this study, we present the first application of the YOLOv5m lightweight semantic segmentation model with convolutional block attention module (CBAM) for classifying DOM macerals into five distinct categories, namely vitrinite, inertinite, solid bitumen, sporinite, and alginite. The results demonstrate that the YOLOv5m + CBAM model exhibits a superior convergence rate, as evidenced by the loss and precision-recall curves. It also achieves a 2 % increase in mean average precision at 0.5 intersection over union threshold over the YOLOv5m model. Moreover, the YOLOv5m + CBAM model shows robustness and efficiency in DOM macerals segmentation, achieving a mean intersection over union of 53.4 % and a mean pixel accuracy of 72.2 % (excluding the background category), while maintaining similar parameters and floating-point operations comparable to those of YOLOv5m. Comparative analysis against models such as DeepLab v3 + and U-net reveals that YOLOv5m + CBAM achieves superior accuracy with fewer parameters, particularly on smaller datasets. Statistical analysis confirms its consistency with manual labeling, resulting in a mean absolute percentage error of 7.34 %. Consequently, the proposed YOLOv5m + CBAM framework represents an ideal approach for pixel segmentation of DOM macerals.

A lightweight feature extraction backbone for gangue detection with improved Resnet50

ABSTRACT In response to the sizable parameters and floating point operations (FLOPs) of feature extraction backbone of gangue detection algorithm, a lightweight backbone was proposed by improving Resnet50. Initially, the partial 3 × 3 convolution was used to replace regular 3 × 3 convolution in original Resnet50 for reducing parameters and FLOPs in convolution operation. Subsequently, layers of batch normalization and rectified linear unit after 3 × 3 convolution were removed for their further reduction. Finally, bottleneck attention module was integrated at the bottleneck of Resnet50 to improve the mean average precision (mAP) in gangue detection. Based on three proposed methods, the ablation and comparative experiments were carried out. The reduction in parameters and FLOPs and increase in mAP were analyzed and discussed according to experimental results. It could be concluded that the parameters and FLOPs of the obtained lightweight backbone were no more than 56.36% and 57.42% of those of the popular backbones, while the decrease amplitude of its mAP value was no more than 0.2%.

Lightweight and accurate aphid detection model based on an improved deep-learning network

Rapid and accurate detection of bamboo aphids can help prevent large-scale aphid infestations from occurring, which is of great significance for increasing bamboo shoot production and economic benefits. Herein, a lightweight and accurate model, SCA-YOLOv5s, was established by integrating ShuffleNetv2 and Coordinate Attention with the YOLOv5s model to detect Takecallis taiwanus on the yellow sticky traps. Specifically, we first replaced the backbone network of YOLOv5s with ShuffleNetv2 to reduce the number of parameters and computational complexity of the model. Second, an anchor optimization method was proposed by combining linear scaling and k-means algorithm to generate appropriate anchor boxes for detecting small-sized alate aphids. Third, the coordinate attention mechanism was added to the neck network to improve the feature extraction ability. To verify the performance of the proposed SCA-YOLOv5s model, eight detection models were constructed with existing deep learning methods, including SSD300, YOLOv3, Faster R-CNN, YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, YOLOv7-Tiny, and YOLOv5s. Results reveal that the SCA-YOLOv5s model achieved higher detection accuracy than the other eight models. Its mean average precision reached 92.2 %. The proposed model has a size of only 6.7 MB, its floating-point operations (FLOPs) is 7.4 × 109, its inference time is 6.6 ms, and compared with YOLOv5s, it is 53.47 % smaller in model size, 55.15 % lower in FLOPs, and 0.8 ms faster in inference time. The results indicate that the proposed model can maintain high detection accuracy while minimizing computation and inference time, which is crucial for deployment in remote areas with low information technology. This study provides valuable technical support for the control of aphids in bamboo forests.

Leaf rolling detection in maize under complex environments using an improved deep learning method

Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.

Real-time tilapia fillet defect segmentation on edge device for robotic trimming

The implementation of robotic tilapia fillet trimming instead of manual labor is a pivotal advancement in intelligent fish processing, offering substantial superiority in operational efficiency and product quality. The study presents an improved model called TFDS-YOLOv8n for tilapia fillet defects segmentation. The model incorporates Coordinate Attention (CA) into the feature extraction layer, thereby enhancing its ability to capture characteristics at various levels of the input. Additionally, the feature fusion layer is reconstructed as Slim-Neck to reduce the number of parameters without compromising prediction accuracy. Furthermore, the bounding box loss function is modified by MPDIoU to expedite the model convergence. The proposed model was tested employing the dataset collected from the practical tilapia processing plant. Ablation experiments demonstrate that TFDS-YOLOv8n achieves a reduction of 0.29 MB in parameters and 1 G in Floating Point Operations (FLOPs) while increasing bbox_mAP and mask_mAP by 2.8 % and 2.5 %, respectively. Eventually, the model is further accelerated by TensorRT and deployed on the edge device. Experimental results show that the quantized model possesses a faster inference speed than the untransformed model, which realizes real-time detection of tilapia fillet defects and facilitates robotization in fish processing.

Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs.

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S-1. This factorization of S-1, written as ZZT = S-1, is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix-matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

PCB Defect Detection Based on Improved Deep Learning Model

Printed circuit boards (PCBs) play a critical role in electronic products. Ensuring these products’ long-term reliability and consistent performance requires effective PCB defect detection. Although existing deep learning models for PCB defect detection are not highly accurate, they often neglect capability considerations. This paper introduces a precise, fast, and lightweight defect detection model, CCG-YOLO, based on an enhanced YOLOv5 model to address this issue. The enhancements in CCG-YOLO can be summarized as follows: (1) Improved Backbone network: The feature extraction ability of the Backbone network is enhanced by introducing a C3HB module, which fosters spatial interaction capabilities. (2) Lightweight feature fusion network: A lightweight convolution structure called Ghost-Shuffle Convolution is incorporated in the feature fusion network, remarkably reducing model parameters while maintaining performance. (3) Efficient residual networking: To enhance model performance further, a CNeB module is introduced based on the ConvNeXt network, which replaces the C3 module in the Neck. CNeB improves model detection accuracy and reduces the number of model parameters. The combination of these enhancements results in impressive performance. CCG-YOLO achieves mean average precision (mAP@0.5) of 99.5% and 88.75% in mAP@0.5:0.95 on the TDD-Net public dataset. Compared with the original YOLOv5s algorithm, CCG-YOLO offers a 4.24% improvement in mAP@0.5:0.95, a 1[Formula: see text]MB reduction in model size, a 0.472[Formula: see text]M decrease in the number of parameters, a 0.6G floating point operation reduction in computational complexity, and a 120 frames per second real-time inference speed. These experimental results underscore that the proposed model excels in accuracy and speed and has a compact size for PCB defect detection. Moreover, CCG-YOLO is easily deployable on low-end devices, making it well-suited for meeting the real-time requirements of industrial defect detection.

A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on YOLOv8s.

As one of the most important economic crops, grapes have attracted considerable attention due to their high yield, rich nutritional value, and various health benefits. Identifying grape bunches is crucial for maintaining the quality and quantity of grapes, as well as managing pests and diseases. In recent years, the combination of automated equipment with object detection technology has been instrumental in achieving this. However, existing lightweight object detection algorithms often sacrifice detection precision for processing speed, which may pose obstacles in practical applications. Therefore, this thesis proposes a lightweight detection method named YOLOv8s-grape, which incorporates several effective improvement points, including modified efficient channel attention (MECA), slim-neck, new spatial pyramid pooling fast (NSPPF), dynamic upsampler (DySample), and intersection over union with minimum point distance (MPDIoU). In the proposed method, MECA and NSPPF enhance the feature extraction capability of the backbone, enabling it to better capture crucial information. Slim-neck reduces redundant features, lowers computational complexity, and effectively reuses shallow features to obtain more detailed information, further improving detection precision. DySample achieves excellent performance while maintaining lower computational costs, thus demonstrating high practicality and rapid detection capability. MPDIoU enhances detection precision through faster convergence and more precise regression results. Experimental results show that compared to other methods, this approach performs better in the grapevine bunch detection dataset and grapevine bunch condition detection dataset, with mean average precision (mAP50-95) increasing by 2.4% and 2.6% compared to YOLOv8s, respectively. Meanwhile, the computational complexity and parameters of the method are also reduced, with a decrease of 2.3 Giga floating-point operations per second and 1.5 million parameters. Therefore, it can be concluded that the proposed method, which integrates these improvements, achieves lightweight and high-precision detection, demonstrating its effectiveness in identifying grape bunches and assessing biophysical anomalies.

SSL-LRN: A Lightweight Semi-Supervised-Learning-Based Approach for UWA Modulation Recognition

Due to the lack of sufficient valid labeled data and severe channel fading, the recognition of various underwater acoustic (UWA) communication modulation types still faces significant challenges. In this paper, we propose a lightweight UWA communication type recognition network based on semi-supervised learning, named the SSL-LRN. In the SSL-LRN, a mean teacher–student mechanism is developed to improve learning performance by averaging the weights of multiple models, thereby improving recognition accuracy for insufficiently labeled data. The SSL-LRN employs techniques such as quantization and small convolutional kernels to reduce floating-point operations (FLOPs), enabling its deployment on underwater mobile nodes. To mitigate the performance loss caused by quantization, the SSL-LRN adopts a channel expansion module to optimize the neuron distribution. It also employs an attention mechanism to enhance the recognition robustness for frequency-selective-fading channels. Pool and lake experiments demonstrate that the framework effectively recognizes most modulation types, achieving a more than 5% increase in recognition accuracy at a 0 dB signal-to-noise ratio (SNRs) while reducing FLOPs by 84.9% compared with baseline algorithms. Even with only 10% labeled data, the performance of the SSL-LRN approaches that of the fully supervised LRN algorithm.

YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems.

Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model's stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection.

Swin transformer adaptation into YOLOv7 for road damage detection

Highways are an important component of any country. However, some highways in Indonesia endanger users while maintaining road safety. Crack detection early in the deterioration process can prevent further damage and lower maintenance costs. A recent study sought to develop a method for detecting road damage by combining the road damage detection (RDD) dataset with generative adversarial network technology and data augmentation to improve training. The current study aims to broaden the you only look once (YOLO) framework by incorporating the Swin Transformer into the chiral stationary phases (CSP) component of YOLOv7, with the goal of improving object detection accuracy in a variety of visual scenarios. The study compares the performance of various object detection models with varying parameters and configurations, such as YOLOv5l, YOLOv6l, YOLOv7-tiny, YOLOv7, and YOLOv7x. YOLOv5l has 46 million parameters and 108 billion floating point operations per second (FLOPS), whereas YOLOv6l has 59.5 million parameters and 150 billion FLOPS. With 31 million parameters and 140 billion FLOPS, the YOLOv7-swin model performs best with mean average precision (mAP), mAP_0.50 of 0.47. and mAP_0.5:0.95 of 0.232. The experimental results show that our YOLOv7-swin model outperforms both YOLOv7x and YOLOv7-tiny. The proposed model significantly improves object detection accuracy while keeping complexity and performance in balance.

Fostc3net: A lightweight YOLOv5 based on the network structure optimization

Abstract Transmission line detection technology is crucial for automatic monitoring and ensuring the safety of electrical facilities. The YOLOv5 series is currently one of the most advanced and widely used methods for object detection. However, it faces inherent challenges, such as high computational load on devices and insufficient detection accuracy. To address these concerns, this paper presents an enhanced lightweight YOLOv5 technique customized for mobile devices, specifically intended for identifying objects associated with transmission lines. The C3Ghost module is integrated into the convolutional network of YOLOv5 to reduce floating point operations per second (FLOPs) in the feature channel fusion process and improve feature expression performance. In addition, a FasterNet module is introduced to replace the c3 module in the YOLOv5 Backbone. The FasterNet module uses Partial Convolutions to process only a portion of the input channels, improving feature extraction efficiency and reducing computational overhead. To address the imbalance between simple and challenging samples in the dataset and the diversity of aspect ratios of bounding boxes, the wIoU v3 LOSS is adopted as the loss function. To validate the performance of the proposed approach, Experiments are conducted on a custom dataset of transmission line poles. The results show that the proposed model achieves a 1% increase in detection accuracy, a 13% reduction in FLOPs, and a 26% decrease in model parameters compared to the existing YOLOv5. In the ablation experiment, it was also discovered that while the Fastnet module and the CSghost module improved the precision of the original YOLOv5 baseline model, they caused a decrease in the mAP@.5-.95 metric. However, the improvement of the wIoUv3 loss function significantly mitigated the decline of the mAP@.5-.95 metric.

Research note: A method for recognizing and evaluating typical behaviors of laying hens in a thermal environment

Automatically identifying abnormal behaviors of caged laying hens in a thermal environment improves manual management efficiency. It also provides reference indicators for breeding heat-tolerant hens. In this study, we propose a deep learning-based method for automatic recognition and evaluation of typical heat stress behaviors in hens. We developed a lightweight object detection algorithm, YOLO-HGP, based on the YOLOv8n as the baseline model. YOLO-HGP achieves Precision (P), Recall (R), and mean average precision (mAP) of 95.952%, 94.127%, and 97.667%, respectively, effectively detecting typical heat stress behaviors in hens. Compared to the original YOLO v8n, YOLO-HGP improves R, and mAP by 6.257%, and 1.963%, respectively. The FLOPs (floating point operations) and parameter count of YOLO-HGP are 4.3G and 1.729M, reducing by 47.56% and 42.58% compared to the original model. Additionally, we introduce the "ORC-ratio" (The ratio of the combined frequency of open-beak breathing and retching behaviors to the frequency of closed-beak behaviors.) as an evaluation indicator for the frequency of typical heat stress behaviors in hens and combine it with the Hybrid-SORT multiobject tracking algorithm to achieve tracking detection of individual hens. The study demonstrates that the proposed model effectively identifies and quantitatively evaluates typical behaviors of hens in a thermal environment, providing an effective approach for the automated recognition of heat stress behaviors in hens.