YOLOv5-GE Vehicle Detection Algorithm Integrating Global Attention Mechanism

With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.

Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s is proposed to enhance the detection accuracy for small targets such as apple fruits. By applying improvements to the RFA module, DFP module, and Soft-NMS algorithm, as well as integrating these three modules together, accurate detection of small targets in images can be achieved. Experimental results demonstrate that the integrated, improved model achieved a significant improvement in detection accuracy, with precision, recall, and mAP increasing by 3.6%, 6.8%, and 6.1%, respectively. Furthermore, the improved method shows a faster convergence speed and lower loss value during the training process, resulting in higher recognition accuracy. The results of this study indicate that the proposed improved method exhibits a good performance in apple fruit detection tasks involving UAV imagery, which is of great significance for fruit yield estimation. The research findings demonstrate the effectiveness and feasibility of the improved method in addressing small target detection tasks, such as apple fruit detection.

Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios.

This study aims to solve the low detection accuracy and susceptibility to false detection and omission in pedestrian and vehicle detection by proposing an improved YOLOv5s algorithm. Firstly, a small target detection module is added to better acquire and determine the information of pedestrians from long-range vehicles. Secondly, the multi-scale channel attention CBAM attention module is added, and the dual attention mechanism is not only flexible and convenient, but also improves the computational efficiency. Finally, the MPDIoU loss function based on minimum point distance is introduced to replace the original GIoU loss function, and this change not only enhances the regression accuracy of the model. At the same time, the convergence speed of the model is accelerated. KITTI data set was used for experiments, and the experimental results showed that the average accuracy of the model trained by the improved YOLOv5s algorithm on the data set reached 84.9%, which was 3.7% higher than that of the original YOLOv5s algorithm. It is verified that the model is suitable for high accuracy of pedestrian and vehicle recognition in complex environments, and has high value for promotion.

Estimation of traffic flow parameters based on computer vision is still a very popular challenge. In particular, the detection problems such as occlusion, small targets, luminance variations, and lighting jitter, etc., are caused by dense traffic scenes and complex weather conditions. Previous studies about detecting and tracking methods focused on daytime and nighttime or single weather condition, but the performance of this model will decline dramatically due to complex conditions. In this paper, a framework for estimating traffic flow parameters in complex environments is presented, in which Ghost-YOLOv8 vehicle detection model is proposed. GhostConv is used to replace part of Conv and a new C2fGhost module is designed to replace part of C2f, reducing the number of parameters and improving the detection performance of the model; The global attention mechanism module is added to the neck network, which strengthens the semantic and location information in the features and improves the model’s ability of feature fusion; In view of the loss of semantic information caused by different scales in detecting small targets, a small target detection layer is added to enhance a combination of deep and shallow semantic information; The GIoU bounding loss function is used instead of the original loss function, which improves the performance of the network’s bounding box regression. Then, combining DeepSORT tracking algorithm and virtual line counting method, the proposed framework estimates traffic flow parameters, including volume, speed, and density in complex environments. The experimental results show that compared with the original model in the UA-DETRAC dataset, the improved YOLOv8 model’s the accuracy is increased by 1.4%, and the parameter number and model size are reduced by 0.229G and 0.2MB respectively. In addition, the framework has good robustness, reaching 97.56% accuracy when estimating average traffic flow parameters. It is sufficient to overcome complex weather conditions and effectively help control traffic for intelligent transportation systems and traffic management to provide good data support. In conclusion, it shows that the model can reduce the number and size of model parameters and improve the detection precision as well as meeting the requirements of edge computing devices and having better real-time performance, so it has practical application value.

In remote sensing and low-altitude unmanned aerial vehicle(UAV) detection scenarios, small target detection is extremely challenging due to the low pixel proportion, sparse features, and complex backgrounds of targets. The reliability of low-altitude security, in particular, is directly dependent on the accuracy of small target detection. However, current methods still face three major limitations: insufficient detection accuracy for targets smaller than 20 pixels; artifacts and false textures introduced by Generative Adversarial Network-based enhancement, which lead to increased false detection rates; and the reliance of existing approaches on specialized architectures, resulting in weak generalization capability and difficulty in adapting to multi-scenario deployment requirements. To address these issues, this paper proposes a plug-and-play dual-mechanism collaborative enhancement framework named HD-BSNet. Firstly, a High-Frequency Differential Perception mechanism is designed to enhance the detailed feature representation of small targets. Secondly, a Background Semantic Modeling mechanism is introduced to learn key features that distinguish targets from the background. Additionally, a Parallel Multi-Scale Focus Module is constructed to further reinforce target features. Extensive experiments on three small target datasets demonstrate that the proposed method effectively improves the accuracy and generalization ability of small target detection.

Aiming at the problems of small target size, dense target, missing detection and false detection in UAV aerial images, an improved YOLOv8s small target detection algorithm MS-P2-YOLO is proposed in this paper. First, through several initial convolution layers, the composite convolution module extracts the target feature information, and uses pooling operations of different scales to capture the global context information of the image. Then, the scale adaptive fusion unit module is used to scale and splicing the feature map in a certain form to integrate the feature information from different scales or layers. Then the multi-dimensional feature integration module is used to adjust the features according to the size or number of channels of the input feature map, and to enhance the feature representation through certain forms of scaling and sequence processing. At the same time, P2 detection head is added at the end to further increase the detection ability of small targets, which greatly improves the problems of missing detection, false detection and large number of parameters of small targets. Experiments show that compared with the VisDrone2019 dataset of MS-P2-YOLO and YOLOv8s, P, R and mAP50% have increased by 9%, 12.9% and 12.9% respectively, and the number of parameters has decreased by 4%. At the same time, generalization and comparison experiments were also conducted in YOLOv5s, YOLOv8s, ATSS, TOOD, and Father-RCNN, and the detection results were visualized. The experiments showed that all parameters were improved.

In the realm of unmanned aerial vehicles, we proposed an enhanced small target detection algorithm, MGAC-YOLO, to address the challenges of missed detections and low accuracy associated with small target identification. Initially, we designed the MConv (Multi-layer Convolution) module to replace the conventional Conv module within the backbone network, thereby augmenting the dimensionality of information capture and enhancing the detection performance for small targets. Subsequently, we harnessed the advantages of both attention mechanisms-GAM (Global Attention Mechanism) and CloAttention (Contextualized Local and Global Attention)-to create a GACAttention module that extracts small target features from both global and local perspectives, thereby enriching the network's focus on small target feature information and further enhancing its feature processing capabilities. Finally, we incorporated an additional small target detection layer to capture feature information at a shallower level, thereby reducing the likelihood of missed detections and bolstering the detection capabilities for small targets. Experimental results on the VisDrone2019 dataset demonstrate that the Precision, mAP50, and mAP50-95 of the MGAC-YOLO algorithm have improved by 5.3%, 6.3%, and 4.4%, respectively, in comparison to the baseline model YOLOv8s. Furthermore, when compared to other leading algorithms, the MGAC-YOLO algorithm has exhibited notable superiority.

针对图像序列中运动目标检测、跟踪的难点问题，提出了一种实时运动目标检测与跟踪算法。该算法基于自适应背景建模，获取运动目标背景模型和前景图像，从而实现运动目标检测；通过建立运动目标的位置、大小、形状以及颜色分布模型，构造运动目标全局匹配函数，结合目标活力特征，实现多运动目标连续匹配和跟踪。实验结果表明，相对于传统的运动目标跟踪方法，本文方法明显减少了运算时间，增强了环境适应性，实现了复杂场景下运动目标的准确检测和稳定跟踪，对非刚性目标的形变、旋转具有较强的鲁棒性。In this paper, we propose a real-time mobile target detection and tracking algorithm for challenges of mobile target detection and tracking in sequential images. This algorithm based on the adaptive background modeling obtains background model and front-view images of mobile targets, which is the way to achieve target detection. Continuous matching and tracking of multiple mobile targets are realized through constructing position, size, shape and color distribution of the mobile targets, defining a global matching function for those targets, and associating their vitality characteristics. It is demonstrated by experiments that the algorithm presented in this paper, compared to the traditional methods of mobile target tracking, significantly reduces the computation time, improves adaptive feature to environments, achieves accurate detection and robust tracking of mobile targets in complex environments, and shows strong robustness to deformation and rotation of non-rigid targets.

Accurate bud detection is a prerequisite for automatic tea picking and yield statistics; however, current research suffers from missed detection due to the variety of singleness and false detection under complex backgrounds. Traditional target detection models are mainly based on CNN, but CNN can only achieve the extraction of local feature information, which is a lack of advantages for the accurate identification of targets in complex environments, and Transformer can be a good solution to the problem. Therefore, based on a multi-variety tea bud dataset, this study proposes RT-DETR-Tea, an improved object detection model under the real-time detection Transformer (RT-DETR) framework. This model uses cascaded group attention to replace the multi-head self-attention (MHSA) mechanism in the attention-based intra-scale feature interaction (AIFI) module, effectively optimizing deep features and enriching the semantic information of features. The original cross-scale feature-fusion module (CCFM) mechanism is improved to establish the gather-and-distribute-Tea (GD-Tea) mechanism for multi-level feature fusion, which can effectively fuse low-level and high-level semantic information and large and small tea bud features in natural environments. The submodule of DilatedReparamBlock in UniRepLKNet was employed to improve RepC3 to achieve an efficient fusion of tea bud feature information and ensure the accuracy of the detection head. Ablation experiments show that the precision and mean average precision of the proposed RT-DETR-Tea model are 96.1% and 79.7%, respectively, which are increased by 5.2% and 2.4% compared to those of the original model, indicating the model’s effectiveness. The model also shows good detection performance on the newly constructed tea bud dataset. Compared with other detection algorithms, the improved RT-DETR-Tea model demonstrates superior tea bud detection performance, providing effective technical support for smart tea garden management and production.

With the rapid development of autonomous driving technology, traditional object detection algorithms often face challenges such as the trade-off between model parameters and recognition accuracy, insufficient detection accuracy, and slow detection speed when dealing with vehicle and pedestrian detection tasks in complex scenarios. To address these challenges, we propose a novel lightweight real-time driving scene detection network, FEEP-YOLO. Based on the YOLOv8 algorithm, our network not only maintains high detection accuracy but also achieves faster training speed and fewer model parameters. FEEP-YOLO employs a lightweight network, Faster Net, to significantly reduce model parameters, thereby accelerating detection speed and better meeting real-time requirements. To further improve detection accuracy, we introduce an EMA attention module into the network and optimize the bounding box loss function, adopting the EIOU loss function to enhance target features and reduce background interference. Additionally, to detect small objects more accurately, we add feature layers rich in semantic information to the detection end. Experimental results demonstrate that FEEP-YOLO outperforms YOLOv8 in terms of both detection speed and accuracy on the KITTI and SODA datasets, while significantly reducing the number of model parameters and model size. FEEP-YOLO achieves high detection speed while improving detection accuracy and reducing computational resource consumption, meeting the practical application requirements under limited computing power, memory space and power consumption. The key contribution of FEEP-YOLO lies in its ability to balance accuracy speed, and efficiency, making it a robust solution for real-time autonomous driving applications in complex environments.

The problems of low detection accuracy and high leakage rate of small targets on the water’s surface when observed from the viewpoint of an uncrewed ship are proposed to be resolved by an improved YOLOv7 detection algorithm. Firstly, the parameter-free attention mechanisms SimAM and PConv are introduced into the network to enhance the model’s feature extraction for small targets and reduce the number of model parameters, and SimAM and PConv in the backbone are combined with ELAN1 to form a new efficient layer aggregation network module ELAN1-SAPC. Then, the Head part of the model adopts the newly proposed MP-CXOS structure, which can enhance the model’s focus on small targets and effectively reduce the loss of small target feature information. Finally, the loss function is optimized by incorporating the new NWD metric approach to improve the model’s ability to detect small targets and speed up network convergence. The data set is also processed using data augmentation to improve the model’s generalization. The experiments show that compared with the basic model, the mean Average Precision of the improved YOLOv7 model increases by 5.3 per cent, the F1 score increases by 0.06, and the number of parameters decreases by 20.0 per cent. Therefore, the improved model has significantly improved detection performance and is more lightweight.

PE-Transformer: Path enhanced transformer for improving underwater object detection

In order to reduce the probability of vehicle rear end collision accident in foggy weather, and in accordance with the background of accelerating the development of automobile safety industry with artificial intelligence technology, a vehicle recognition, detection and voice early warning based on improved yolov5 algorithm is proposed. Firstly, adaptive convolution is introduced into convolution neural network to enhance the feature extraction ability of target in the process of training network model, and then improve the expression ability of neural network for important feature channels, especially for small target recognition and detection; Then, considering the real-time nature of detection and early warning, the neck detection layer in yolov5s network is cut and optimized to improve the reasoning speed of the network model. Finally, through the comparative analysis of the evaluation indexes such as the accuracy, recall and map of the training model, and the test of the scene pictures of the actual driver's perspective in foggy weather, it is verified that the improved yolov5 algorithm can improve the detection ability and reasoning speed of small target vehicles. The experimental results show that the accuracy of the improved yolov5 algorithm is as high as 97% and the reasoning speed is improved by 1/3. The driver can be warned in real time to reduce the probability of rear end collision accidents.

The agricultural scene is a typical unstructured scene, which is intricate and heavily affected by sunlight, weather, and other factors. Agricultural segmentation targets are generally small in size and heavily obstructed. At the same time, image segmentation in agricultural scenes has strong application scenarios, such as blooming intensity estimation, which refers to the estimation of the density and intensity of blooms, fruit yield estimation, fruit harvesting positioning, and so on. Currently, CNNs dominate semantic segmentation of agricultural scenes due to the significant computational constraints of using the Transformer module. However, CNNs have several disadvantages, such as limited effective receptive fields and the inability to capture global information, which significantly reduce their segmentation accuracy in complex agricultural scenes. In addition, the simple upsampling process used in CNNs can result in blurred segmentation edges and inferior performance. This paper presents a new semantic segmentation algorithm based on SegFormer: PD-SegNet(Powerful Decoder SegFormer Network), which balances accuracy and computational efficiency and combines dynamic kernel self-renewal with edge-aware optimization. The proposed algorithm demonstrates outstanding performance in two typical agricultural scenarios: apple blossom and apple fruit segmentation detection and sets a new state-of-the-art (SOTA) on the MinneApple Apple segmentation dataset. Experimental results demonstrate that the proposed method outperforms the baseline method in the segmentation of complex small targets. This algorithm can optimize the semantics segmentation of small targets in complex scenes and contributes to the development of smart agriculture.