Small Object Detection Research Articles

A small underwater object detection model with enhanced feature extraction and fusion

In the underwater domain, small object detection plays a crucial role in the protection, management, and monitoring of the environment and marine life. Advancements in deep learning have led to the development of many efficient detection techniques. However, the complexity of the underwater environment, limited information available from small objects, and constrained computational resources make small object detection challenging. To tackle these challenges, this paper presents an efficient deep convolutional network model. First, a CSP for small object and lightweight (CSPSL) module is introduced to enhance feature retention and preserve essential details. Next, a variable kernel convolution (VKConv) is proposed to dynamically adjust the convolution kernel size, enabling better multi-scale feature extraction. Finally, a spatial pyramid pooling for multi-scale (SPPFMS) method is presented to preserve the features of small objects more effectively. Ablation experiments on the UDD dataset demonstrate the effectiveness of the proposed methods. Comparative experiments on the UDD and DUO datasets demonstrate that the proposed model delivers the best performance in terms of computational cost and detection accuracy, outperforming state-of-the-art methods in real-time underwater small object detection tasks.

LKR-DETR: small object detection in remote sensing images based on multi-large kernel convolution

LKR-DETR: small object detection in remote sensing images based on multi-large kernel convolution

FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection

FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection

An Investigation of Infrared Small Target Detection by Using the SPT–YOLO Technique

To detect and recognize small-size and submerged complex background targets in infrared images, we combine a dynamic receptive field fusion strategy and a multi-scale feature fusion mechanism to improve the detection performance of small targets significantly. The space-to-depth convolution module is introduced as a downsampling layer in the backbone first and achieves the same sampling effect. More detailed information is retained at the same time. Thus, the model’s detection capability for small targets has been enhanced. Then, the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampling. Furthermore, x-small detection heads are added, the understanding of the overall characteristics and structure of the target is enhanced much more, and the representation and localization of small targets have been improved. Finally, the cross-entropy loss function in the original network model is replaced by an adaptive threshold focal loss function, forcing the model to allocate more attention to target features. The above methods are based on a public tool, the eighth version of You Only Look Once (YOLO) improved, it is named SPT–YOLO (SPDConv + P2 + Adaptive Threshold + YOLOV8s) in this paper. Some experiments on datasets such as infrared small object detection (IR-SOD) and infrared small target detection 1K(IRSTD-1K), etc. have been executed to verify the proposed algorithm; and the mean average precision of 94.0% and 69% under the condition of threshold at 0.5 and over a range from 0.5 to 0.95 is obtained, respectively. The results show that the proposed method achieves the best performance of infrared small target detection compared to existing methods.

Advances and Challenges in Small Object Detection: A Comparative Analysis of State-of-the-Art Models and Future Directions

Object detection involves the precise and efficient identification and localization of multiple predefined object categories within images. With the advent of deep learning, both the accuracy and efficiency of object detection have significantly improved. Nevertheless, challenges remain in optimizing the performance of mainstream detection algorithms, improving the accuracy of small object detection, enabling multi-class detection, and developing lightweight models. In response to these challenges, this paper provides a comprehensive literature review, analyzing approaches to enhance mainstream object detection by exploring advancements in backbone networks, expanding the visual receptive field, feature fusion, and various training strategies. We also evaluate the performance of leading detection models across established datasets, identifying current limitations and proposing future research directions. These directions include enhancing small object representation in datasets, enriching semantic information, and improving model interpretability. Small object detection remains a critical focus in computer vision, and we anticipate the continued development of algorithms with higher accuracy and efficiency

A lightweight multidimensional feature network for small object detection on UAVs

A lightweight multidimensional feature network for small object detection on UAVs

Steel surface defect detection based on improved YOLOv8

Abstract Addressing the issue of imbalance between detection accuracy and speed in current steel surface defect
detection methods, we propose an improved YOLOv8-based algorithm, named YOLO-SSD, for steel surface defect
detection. This algorithm replaces the downsampling module with a SPD-Conv module in the backbone network and
introduces the CBAM attention module to enhance feature extraction capability and small object detection ability.
Additionally, in the neck network, the C2f module is replaced with a C2f-Ghost module, which not only lightweightens
the neck module but also improves the utilization of computational resources. Experimental results on the NEU DET dataset demonstrate that compared to the original YOLOv8 algorithm, the average detection accuracy of the
YOLO-SSD algorithm for steel surface defect detection has been improved by 2.8%, with a frame per second (FPS)
reaching 96. Compared to current mainstream steel surface defect detection algorithms, the improved detection
algorithm can enhance the detection accuracy of the defect detection model without compromising detection speed.

SCM-YOLO for Lightweight Small Object Detection in Remote Sensing Images

Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need to balance performance and complexity when improving the model. To address these issues, this paper proposes an efficient and lightweight SCM-YOLO detector improved from YOLOv5 with spatial local information enhancement, multi-scale feature adaptive fusion, and global sensing capabilities. The SCM-YOLO detector consists of three innovative and lightweight modules: the Space Interleaving in Depth (SPID) module, the Cross Block and Channel Reweight Concat (CBCC) module, and the Mixed Local Channel Attention Global Integration (MAGI) module. These three modules effectively improve the performance of the detector from three aspects: feature extraction, feature fusion, and feature perception. The ability of SCM-YOLO to detect small objects in complex remote sensing environments has been significantly improved while maintaining its lightweight characteristics. The effectiveness and lightweight characteristics of SCM-YOLO are verified through comparison experiments with AI-TOD and SIMD public remote sensing small object detection datasets. In addition, we validate the effectiveness of the three modules, SPID, CBCC, and MAGI, through ablation experiments. The comparison experiments on the AI-TOD dataset show that the mAP50 and mAP50-95 metrics of SCM-YOLO reach 64.053% and 27.283%, respectively, which are significantly better than other models with the same parameter size.

PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection

Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model—PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection. By extracting the feature information of small objects in the high-resolution stage of the image, the P2 layer helps the network better capture small objects. At the same time, in order to reduce unnecessary calculations and lower the complexity of the model, we removed the P5 layer. In addition, we have introduced the coordinate spatial attention mechanism, which can help the network more accurately obtain the spatial and positional features required for small targets, thereby further improving detection accuracy. In the VisDrone2019 datasets, experimental results show that PC-YOLO11s outperforms other existing YOLO-series models in overall performance. Compared with the baseline YOLO11s model, PC-YOLO11s mAP@0.5 increased from 39.5% to 43.8%, mAP@0.5:0.95 increased from 23.6% to 26.3%, and the parameter count decreased from 9.416M to 7.103M. Not only that, we also applied PC-YOLO11s to tea bud datasets, and experiments showed that its performance is superior to other YOLO-series models. Experiments have shown that PC-YOLO11s exhibits excellent performance in small object detection tasks, with strong accuracy improvement and good generalization ability, which can meet the needs of small object detection in practical applications.

Correction: ODD-YOLOv8: an algorithm for small object detection in UAV imagery

Correction: ODD-YOLOv8: an algorithm for small object detection in UAV imagery

Hybrid Intersection over Union Loss for a Robust Small Object Detection in Low-light Conditions

Hybrid Intersection over Union Loss for a Robust Small Object Detection in Low-light Conditions

Precision and speed: LSOD-YOLO for lightweight small object detection

Precision and speed: LSOD-YOLO for lightweight small object detection

Research on injection molded parts defect detection algorithm based on multiplicative feature fusion and improved attention mechanism

Injection molded parts are increasingly utilized across various industries due to their cost-effectiveness, lightweight nature, and durability. However, traditional defect detection methods for these parts often rely on manual visual inspection, which is inefficient, expensive, and prone to errors. To enhance the accuracy of defect detection in injection molded parts, a new method called MRB-YOLO, based on the YOLOv8 model, has been proposed. This method introduces several key improvements: (1) the MAFHead, a four-detection head based on multiplicative feature fusion, which replaces the original detection head to enhance feature representation; (2) the RepGFPN-SE module, a re-parameterized generalized feature pyramid network that improves detection of small objects by replacing the original C2f. module; (3) and the BiNorma module, employing a bi-level routing attention mechanism to optimize the training process by reducing input distribution changes across layers. The effectiveness of the MRB-YOLO model was validated through ablation and contrast experiments using a specially constructed dataset of injection molded parts defects. The results demonstrated an accuracy of 88.8%, a recall rate of 86.8%, and a mean average precision (mAP) of 91.5%. Compared to the YOLOv8n model, the MRB-YOLO model shows an increase in accuracy by 8.2%, in recall rate by 17.2%, and in mAP by 11.8%. These findings confirm that the MRB-YOLO model meets the requirements for accurate detection of defects in injection molded parts.

SGI-YOLOv9: an effective method for crucial components detection in the power distribution network

The detection of crucial components in the power distribution network is of great significance for ensuring the safe operation of the power grid. However, the challenges posed by complex environmental backgrounds and the difficulty of detecting small objects remain key obstacles for current technologies. Therefore, this paper proposes a detection method for crucial components in the power distribution network based on an improved YOLOv9 model, referred to as SGI-YOLOv9. This method effectively reduces the loss of fine-grained features and improves the accuracy of small objects detection by introducing the SPDConv++ downsampling module. Additionally, a global context fusion module is designed to model global information using a self-attention mechanism in both spatial and channel dimensions, significantly enhancing the detection robustness in complex backgrounds. Furthermore, this paper proposes the Inner-PIoU loss function, which combines the advantages of Powerful-IoU and Inner-IoU to improve the convergence speed and regression accuracy of bounding boxes. To verify the effectiveness of SGI-YOLOv9, extensive experiments are conducted on the CPDN dataset and the PASCAL VOC 2007 dataset. The experimental results demonstrate that SGI-YOLOv9 achieves a significant improvement in accuracy for small object detection tasks, with an mAP@50 of 79.1% on the CPDN dataset, representing an increase of 3.9% compared to the original YOLOv9. Furthermore, it achieves an mAP@50 of 63.3% on the PASCAL VOC 2007 dataset, outperforming the original YOLOv9 by 1.6%.

YOLO‐Tiny: A lightweight small object detection algorithm for UAV aerial imagery

AbstractIn unmanned aerial vehicle (UAV) aerial target detection tasks, two main challenges exist: the limited computational resources of UAV terminals, which are not conducive to running complex models, and the prevalence of small targets, which can easily lead to missed detections and false positives. To address these issues, this study proposes a lightweight and high‐accuracy small‐object detection algorithm for UAV aerial imagery that is based on YOLOv5s. First, the network structure is optimized by removing the layers in YOLOv5s primarily used for detecting large targets (P4 and P5) and adding layers primarily used for detecting small targets (P2 and P3). This enables the model to focus more on extracting small‐object features. A lightweight dynamic convolution is subsequently introduced in the C3 module, and the lightweight LW_C3 and LW_downsampling modules are designed for feature extraction and downsampling operations. This enhances the model's feature extraction capability while achieving a lightweight design. Finally, the adaptive multi‐scale spatial feature fusion (AMSFF) module is designed to adaptively learn the spatial weights of the feature maps at different levels, thereby further strengthening the effective fusion of multi‐scale features. Experimental results show that the improved YOLO‐Tiny model has higher accuracy and lower complexity, hence validating its excellent performance.

LEN-YOLO: a lightweight remote sensing small aircraft object detection model for satellite on-orbit detection

LEN-YOLO: a lightweight remote sensing small aircraft object detection model for satellite on-orbit detection

Cross-Field Road Markings Detection Based on Inverse Perspective Mapping.

With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road markings data and label them by ourselves manually, which is huge labor work and takes lots of time. Moreover, object detection often encounters the problem of small object detection. The detection accuracy often decreases when the detection distance increases. This is primarily because distant objects on the road take up few pixels in the image and object scales vary depending on different distances and perspectives. For the sake of solving the issues mentioned above, this paper utilizes a virtual dataset and open dataset to train the object detection model and cross-field testing in the field of Taiwan roads. In order to make the model more robust and stable, the data augmentation method is employed to generate more data. Therefore, the data are increased through the data augmentation method and homography transformation of images in the limited dataset. Additionally, Inverse Perspective Mapping is performed on the input images to transform them into the bird's eye view, which solves the "small objects at far distance" problem and the "perspective distortion of objects" problem so that the model can clearly recognize the objects on the road. The model testing on the front-view images and bird's eye view images also shows a remarkable improvement of accuracy by 18.62%.

YOLO-SS: optimizing YOLO for enhanced small object detection in remote sensing imagery

YOLO-SS: optimizing YOLO for enhanced small object detection in remote sensing imagery

YH-RTYO: an end-to-end object detection method for crop growth anomaly detection in UAV scenarios

Background Small object detection via unmanned Aerial vehicle (UAV) is crucial for smart agriculture, enhancing yield and efficiency. Methods This study addresses the issue of missed detections in crowded environments by developing an efficient algorithm tailored for precise, real-time small object detection. The proposed Yield Health Robust Transformer-YOLO (YH-RTYO) model incorporates several key innovations to advance conventional convolutional models. The model features an efficient convolutional expansion module that captures additional feature information through extended branches while maintaining parameter efficiency by consolidating features into a single convolution during validation. It also includes a local feature pyramid module designed to suppress background interference during feature interaction. Furthermore, the loss function is optimized to accommodate various object scales in different scenes by adjusting the regression box size and incorporating angle factors. These enhancements collectively contribute to improved detection performance and address the limitations of traditional methods. Result Compared to YOLOv8-L, the YH-RTYO model achieves superior performance in all key accuracy metrics, with a 13% reduction in the scale of model. Experimental results demonstrate that the YH-RTYO model outperforms others in key detection metrics. The model reduces the number of parameters by 13%, facilitating deployment while maintaining accuracy. On the OilPalmUAV dataset, it achieves a 3.97% improvement in average precision (AP). Additionally, the model shows strong generalization on the RFRB dataset, with AP50 and AP values exceeding those of the YOLOv8 baseline by 3.8% and 2.7%, respectively.

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications.