Multi-scale Object Research Articles

Diffusion model for multi-scale ship object detection and recognition in remote sensing images

Diffusion model for multi-scale ship object detection and recognition in remote sensing images

MRT-YOLO: A Fine-Grained Feature-Based Method for Object Detection

Object detection is an essential component of autonomous driving, unmanned aerial vehicle (UAV) reconnaissance, and other domains. It equips drones and vehicles with the capability to perceive and comprehend their surrounding environment, making it a crucial technology for achieving safe and reliable autonomous driving as well as UAV spot reconnaissance. This paper proposes an end-to-end, high-precision, multi-scale real-time detection algorithm called MRT-YOLO based on YOLOv8. Firstly, in the feature downsampling process of the backbone network, we extend the channel depth to enhance the model’s learning capability for fine-grained features and thereby improve its performance in retaining feature information. Secondly, we enhance the cross-stage partial layer version 2 (C2f) module in YOLOv8 by incorporating a channel self-attention mechanism within it, which optimizes performance through effective feature interaction and integration. Simultaneously, we also employ an improved bidirectional feature pyramid network (BiFPN) and introduce the proposed multi-scale feature learning (MFL) module to further enhance the model’s feature extraction ability. In this study, we fuse the feature maps (C2, C3, C4, and C5) from the backbone network to generate a new feature map C6, thus increasing cross-connections between low-level and high-level features. Lastly, a multi-scale small object detection structure is designed to enhance recognition sensitivity toward densely distributed small objects. The proposed algorithm’s effectiveness and superiority are demonstrated through experiments conducted on two datasets: VisDrone (UAV vision dataset) and BDD100K (automatic driving dataset).

Optimized YOLOv8 for multi-scale object detection

Optimized YOLOv8 for multi-scale object detection

A Directional Enhanced Adaptive Detection Framework for Small Targets

Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing on this area. In this paper, we propose a Directional Enhanced Adaptive (DEA) detection framework for small targets. This framework effectively combines the detection accuracy advantages of two-stage methods with the detection speed advantages of one-stage methods. Additionally, we introduce a Multi-Scale Object Adaptive Slicing (MASA) module and an improved IoU-based aggregation module that integrate with this framework to enhance detection performance. For better comparison, we use the F1 score as one of the evaluation metrics. The experimental results demonstrate that our DEA framework improves the performance of various backbone detection networks and achieves better comprehensive detection performance than other proposed methods, even though our network has not been trained on the test dataset while others have.

MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications

With advancements in autonomous driving, LiDAR has become central to 3D object detection due to its precision and interference resistance. However, challenges such as point cloud sparsity and unstructured data persist. This study introduces MS3D (Multi-Scale Feature Fusion 3D Object Detection Method), a novel approach to 3D object detection that leverages the architecture of a 2D Convolutional Neural Network (CNN) as its core framework. It integrates a Second Feature Pyramid Network to enhance multi-scale feature representation and contextual integration. The Adam optimizer is employed for efficient adaptive parameter tuning, significantly improving detection performance. On the KITTI dataset, MS3D achieves average precisions of 93.58%, 90.91%, and 88.46% in easy, moderate, and hard scenarios, respectively, surpassing state-of-the-art models like VoxelNet, SECOND, and PointPillars.

Multi-kernel inception aggregation diffusion network for tomato disease detection

Tomato leaf diseases significantly impact the yield and quality of tomatoes during cultivation, the main of which are septoria leaf spot, leaf curl virus, verticillium wilt, and early blight. These diseases necessitate prompt detection and management strategies to mitigate their deleterious effects on crop productivity. Due to the considerable scale variations in diseased tomato leaves, accurate and rapid detection and diagnosis remain challenging. To address the detection of tomato leaf diseases at different scales, we propose a real-time detection model incorporating a Multi-kernel Inception Aggregation Diffusion Network. In this paper, (1) We present a Multi-kernel Inception Aggregation Diffusion Network (MIADN) for the feature processing stage, which facilitates the aggregation and diffusion of multi-scale features across hierarchical levels, benefiting the detection of targets at various scales. (2) We present the Multi-kernel Inception Module (MKIM), designed to extract multi-scale object features using diverse convolution kernels, thereby enhancing the model’s feature fusion and representation capabilities. (3) We incorporate the efficient FasterNet network at the feature extraction stage to preserve feature diversity and improve the model’s ability to extract complex target features. (4) Extensive comparative and ablation experiments demonstrate that our method achieves the mean average precision (mAP50) of 96.6%, surpassing the baseline model by 4.1% and the advanced YOLOv9s model by 2.0%. This method provides an effective solution for high-quality tomato cultivation.

I-DINO: High-Quality Object Detection for Indoor Scenes

Object Detection in Complex Indoor Scenes is designed to identify and categorize objects in indoor settings, with applications in areas such as smart homes, security surveillance, and home service robots. It forms the basis for advanced visual tasks including visual question answering, video description generation, and instance segmentation. Nonetheless, the task faces substantial hurdles due to background clutter, overlapping objects, and significant size differences. To tackle these challenges, this study introduces an indoor object detection approach utilizing an enhanced DINO framework. To cater to the needs of indoor object detection, an Indoor-COCO dataset was developed from the COCO object detection dataset. The model incorporates an advanced Res2net as the backbone feature extraction network, complemented by a deformable attention mechanism to better capture detailed object features. An upgraded Bi-FPN module is employed to replace the conventional feature fusion module, and SIoU loss is utilized to expedite convergence. The experimental outcomes indicate that the refined model attains an mAP of 62.3%, marking a 5.2% improvement over the baseline model. These findings illustrate that the DINO-based indoor object detection model exhibits robust generalization abilities and practical utility for multi-scale object detection in complex environments.

Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry.

YOLO-DroneMS: Multi-Scale Object Detection Network for Unmanned Aerial Vehicle (UAV) Images

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

CSSDet: small object detection via cross-scale feature enhancement on drone-view images

ABSTRACT Object detection on drone-view images is vital for applications like intelligent transportation, abnormal behavior detection, and urban surveillance. However, the diverse perspectives and altitudes from which Unmanned Aerial Vehicle (UAV) capture scenes pose challenges due to multi-scale differences and small object sizes. To address this, we propose CSSDet, emphasizing multi-scale object detection and small object enhancement. CSSDet integrates an additional detection head for small objects and employs a bidirectional weighted feature pyramid network for effective cross-scale feature fusion, enhancing global perceptual capability. Additionally, we introduce coordinate attention and Involution modules to mitigate information loss. Experiments are conducted on two publicly available datasets, VisDrone and UAVDT. Quantitative results from the experiments indicate that the proposed CSSDet achieves optimal performance across a wide range of evaluation metrics, with particular strengths demonstrated in multi-scale object detection and small-size object detection. Qualitative results showcase its remarkable effectiveness in various aspects of object detection, including small object detection, objects at different scales and categories, multi-perspective scenarios, and complex scenes. The substantial experimental findings confirm that CSSDet attains comprehensive object detection performance on drone-view images. The source code is available at https://github.com/Gui-Cheng/CSSDet.

Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence

The purpose of this paper is to study the algorithm of object detection and scene understanding in complex scenes assisted by AI(Artificial Intelligence), so as to improve the machine's perception of the surrounding environment. Aiming at occlusion and dense scenes, this paper proposes a series of innovative algorithms by introducing technologies such as multi-scale detection, occlusion processing and real-time optimization. Experimental results show that the proposed algorithm has achieved excellent performance on several public data sets. Compared with the current mainstream object detection algorithm -YOLO, the method proposed in this paper has a significant improvement in accuracy. Especially in multi-scale object detection, occlusion and dense scene processing and real-time optimization, the proposed algorithm shows obvious advantages. The research results provide an effective solution for object detection and scene understanding in complex scenes, and promote the application of AI system in related fields.

Car detection algorithm in urban based on YOLOv5

Car detection is essential to promote the development of intelligent technologies such as autonomous driving and car forward collision warning. To deal with low detection accuracy, poor real-time performance and robustness of existing object detection algorithms, a YOLOv5-based car detection algorithm is used. The advanced structure design of Backbone, Neck and Head in YOLOv5 demonstrates powerful feature extraction and multi-scale object detection capabilities. The detection results reveal that the algorithm is able to quickly recognize cars in images in different urban scenes, and the detection accuracy can reach more than 95%, which can satisfy the application demands for real-time car detection.

SSN: Scale Selection Network for Multi-Scale Object Detection in Remote Sensing Images

The rapid growth of deep learning technology has made object detection in remote sensing images an important aspect of computer vision, finding applications in military surveillance, maritime rescue, and environmental monitoring. Nonetheless, the capture of remote sensing images at high altitudes causes significant scale variations, resulting in a heterogeneous range of object scales. These varying scales pose significant challenges for detection algorithms. To solve the scale variation problem, traditional detection algorithms compute multi-layer feature maps. However, this approach introduces significant computational redundancy. Inspired by the mechanism of cognitive scaling mechanisms handling multi-scale information, we propose a novel Scale Selection Network (SSN) to eliminate computational redundancy through scale attentional allocation. In particular, we have devised a lightweight Landmark Guided Scale Attention Network, which is capable of predicting potential scales in an image. The detector only needs to focus on the selected scale features, which greatly reduces the inference time. Additionally, a fast Reversible Scale Semantic Flow Preserving strategy is proposed to directly generate multi-scale feature maps for detection. Experiments demonstrate that our method facilitates the acceleration of image pyramid-based detectors by approximately 5.3 times on widely utilized remote sensing object detection benchmarks.

ETFE: Efficient transformer with feature enhancement for semantic segmentation

Abstract Despite the powerful capabilities of Transformers in relation to modeling, their effectiveness in capturing multi-scale object features in image-based tasks is often limited. This limitation arises from the diverse shapes and distances between objects in natural scenes, which pose challenges for Transformers to accurately capture object sizes and distances. To overcome this problem, we propose an effective Transformer which extracts multi-scale features at different resolutions. First, we leverage a multi-head attention mechanism to extract richer semantic features at various resolutions. The semantic features are then passed through the Feature Gate to aggregate multi-scale semantic features. Finally, Attentional Feature Enhancement further strengthens the semantic information of target features, enabling the modeling of dependencies among multi-scale features. Comprehensive experiments conducted on the Cityscapes and ADE20K datasets have shown that our model outperforms competing methods in both performance and accuracy.

IV-YOLO: A Lightweight Dual-Branch Object Detection Network.

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images-IV-YOLO-to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond.

YOLOv8s-DDA: An Improved Small Traffic Sign Detection Algorithm Based on YOLOv8s

In the realm of traffic sign detection, challenges arise due to the small size of objects, complex scenes, varying scales of signs, and dispersed objects. To address these problems, this paper proposes a small object detection algorithm, YOLOv8s-DDA, for traffic signs based on an improved YOLOv8s. Specifically, the C2f-DWR-DRB module is introduced, which utilizes an efficient two-step method to capture multi-scale contextual information and employs a dilated re-parameterization block to enhance feature extraction quality while maintaining computational efficiency. The neck network is improved by incorporating ideas from ASF-YOLO, enabling the fusion of multi-scale object features and significantly boosting small object detection capabilities. Finally, the original IoU is replaced with Wise-IoU to further improve detection accuracy. On the TT100K dataset, the YOLOv8s-DDA algorithm achieves mAP@0.5 of 87.2%, mAP@0.5:0.95 of 68.3%, precision of 85.2%, and recall of 80.0%, with a 5.4% reduction in parameter count. The effectiveness of this algorithm is also validated on the publicly available Chinese traffic sign detection dataset, CCTSDB2021.

GMS-YOLO: An Algorithm for Multi-Scale Object Detection in Complex Environments in Confined Compartments.

Many compartments are prone to pose safety hazards such as loose fasteners or object intrusion due to their confined space, making manual inspection challenging. To address the challenges of complex inspection environments, diverse target categories, and variable scales in confined compartments, this paper proposes a novel GMS-YOLO network, based on the improved YOLOv8 framework. In addition to the lightweight design, this network accurately detects targets by leveraging more precise high-level and low-level feature representations obtained from GhostHGNetv2, which enhances feature-extraction capabilities. To handle the issue of complex environments, the backbone employs GhostHGNetv2 to capture more accurate high-level and low-level feature representations, facilitating better distinction between background and targets. In addition, this network significantly reduces both network parameter size and computational complexity. To address the issue of varying target scales, the first layer of the feature fusion module introduces Multi-Scale Convolutional Attention (MSCA) to capture multi-scale contextual information and guide the feature fusion process. A new lightweight detection head, Shared Convolutional Detection Head (SCDH), is designed to enable the model to achieve higher accuracy while being lighter. To evaluate the performance of this algorithm, a dataset for object detection in this scenario was constructed. The experiment results indicate that compared to the original model, the parameter number of the improved model decreased by 37.8%, the GFLOPs decreased by 27.7%, and the average accuracy increased from 82.7% to 85.0%. This validates the accuracy and applicability of the proposed GMS-YOLO network.

A stochastic and analytical model of hierarchical fragmentation

Context. Molecular clouds are the most important incubators of young stars clustered in various stellar structures whose spatial extension can vary from a few AU to several thousand AU. Although the reality of these stellar systems has been established, the physical origin of their multiplicity remains an open question. Aims. Our aim was to characterise these stellar groups at the onset of their formation by quantifying both the number of stars they contain and their mass using a hierarchical fragmentation model of the natal molecular cloud. Methods. We developed a stochastic and predictive model that reconciles the continuous multi-scale structure of a fragmenting molecular cloud with the discrete nature of the stars that are the products of this fragmentation. In this model a gas structure is defined as a multi-scale object associated with a subregion of a cloud. Such a structure undergoes quasi-static subfragmentation until star formation. This model was implemented within a gravo-turbulent fragmentation framework to analytically follow the fragmentation properties along spatial scales using an isothermal and adiabatic equations of state (EOSs). Results. We highlighted three fragmentation modes depending on the amount of fragments produced by a collapsing gas structure, namely a hierarchical mode, a monolithic mode, and a mass dispersal mode. Using an adiabatic EOS we determined a characteristic spatial scale where further fragmentation is prevented, around a few tens of AU. We show that fragmentation is a self-regulated process as fragments tend to become marginally unstable following a M ∝ R Bonnor–Ebert-like mass-size profile. Supersonic turbulent fragmentation structures the cloud down to R ≈ 0.1 pc, and gradually turns into a less productive Jeans-type fragmentation under subsonic conditions so hierarchical fragmentation is a scale dependant process. Conclusions. Our work suggests that pre-stellar objects resulting from gas fragmentation, have to progressively increase their accretion rate in order to form stars. A hierarchical fragmentation scenario is compatible with both the multiplicity of stellar systems identified in Taurus and the multi-scale structure extracted within NGC 2264 molecular cloud. This work suggests that hierarchical fragmentation is one of the main mechanisms explaining the presence of primordial structures of stellar clusters in molecular clouds.

USSC-YOLO: Enhanced Multi-Scale Road Crack Object Detection Algorithm for UAV Image.

Road crack detection is of paramount importance for ensuring vehicular traffic safety, and implementing traditional detection methods for cracks inevitably impedes the optimal functioning of traffic. In light of the above, we propose a USSC-YOLO-based target detection algorithm for unmanned aerial vehicle (UAV) road cracks based on machine vision. The algorithm aims to achieve the high-precision detection of road cracks at all scale levels. Compared with the original YOLOv5s, the main improvements to USSC-YOLO are the ShuffleNet V2 block, the coordinate attention (CA) mechanism, and the Swin Transformer. First, to address the problem of large network computational spending, we replace the backbone network of YOLOv5s with ShuffleNet V2 blocks, reducing computational overhead significantly. Next, to reduce the problems caused by the complex background interference, we introduce the CA attention mechanism into the backbone network, which reduces the missed and false detection rate. Finally, we integrate the Swin Transformer block at the end of the neck to enhance the detection accuracy for small target cracks. Experimental results on our self-constructed UAV near-far scene road crack i(UNFSRCI) dataset demonstrate that our model reduces the giga floating-point operations per second (GFLOPs) compared to YOLOv5s while achieving a 6.3% increase in mAP@50 and a 12% improvement in mAP@ [50:95]. This indicates that the model remains lightweight meanwhile providing excellent detection performance. In future work, we will assess road safety conditions based on these detection results to prioritize maintenance sequences for crack targets and facilitate further intelligent management.

EDASNet: efficient dynamic adaptive-scale network for infrared pedestrian detection

Abstract Infrared images are widely utilized due to their exceptional anti-interference capabilities. However, challenges such as low resolution and an absence of detailed texture can impede the effective recognition of multi-scale target information, particularly for small targets. To address these issues, we introduce a multi-scale detection framework named efficient dynamic adaptive-scale network (EDASNet), which focuses on enhancing the feature extraction of small objects while ensuring efficient detection of multi-scale. Firstly, we design a lightweight dynamic enhance network as the backbone for feature extraction. It mainly includes a lightweight adaptive-weight downsampling module and a dynamic enhancement convolution module. In addition, a multi-scale aggregation feature pyramid network is proposed, which improves the perception effect of small objects through a multi-scale convolution module. Then, the Repulsion Loss term was introduced based on CIOU to effectively solve the missed detection problem caused by target overlap. Finally, the dynamic head was used as the network detection head, and through the superposition of dynamic convolution and multiple attention, the network was able to accurately realize multi-scale object detection. Comprehensive experiments show that EDASNet outperforms existing efficient models and achieves a good trade-off between speed and accuracy.