Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images

Xuan, Chuang; Zhang, Yanwei; Lang, Song; Gong, Yan

doi:10.3390/s23073634

Cited by 24 publications

(6 citation statements)

References 32 publications

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“…Poor detection of small objects is one of the challenges in object detection tasks in the context of UAV aerial photography. In many existing works [ 39 , 40 , 41 , 42 ], detection scales are added to the model to reduce the missed detection rate of small objects, which is an effective improvement method. However, this approach can complicate the structure of the model and increase the consumption of computational and storage resources.…”

Section: Methodsmentioning

confidence: 99%

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

Wang

Chen

et al. 2023

Sensors

260

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Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model’s attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

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Section: Methodsmentioning

confidence: 99%

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

Wang

Chen

et al. 2023

Sensors

260

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“…Previous BM detection studies have used a confidence threshold of 50% [ 9 , 31 , 32 ] or confidence thresholds ranging from 0.1 to 0.9 [ 11 ]; however, these approach may not lead to optimal results for BM detection. Other object detection research has utilized the F1-score, which represents the harmonic mean of precision and recall, to determine the optimal confidence threshold [ 33 , 34 , 35 ]. As the recall is more important than the precision in BM detection, we introduced the F2-score, which emphasizes the importance of recall by assigning it twice the weight of precision, to determine the optimal confidence threshold.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model

Zhou,

Qiu,

Liu

et al. 2023

Cancers

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As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients’ survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.

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“…Additionally, some methods [15] leverage global context information between detected instances and images to eliminate the reliance on anchor boxes and non-maximum suppression (NMS). Moreover, employing attention mechanisms to focus on the surrounding environment of detected instances [16,17] has also yielded promising results in small object detection.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model

An,

Duanmu,

et al. 2024

Aerospace

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Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.

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Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images

Cited by 24 publications

References 32 publications

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model

Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model

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