Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5

Zhou, Jiachen; Su, Taoyong; Li, Kewei; Dai, Jiyang

doi:10.3390/s24010134

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…To solve this problem, Jiang et al [37] proposed an optimization model of a deep neural network, which considers the characteristics of the target in the image and rethinks the construction of the original input data, and improves the detection effect of small-scale rectangular targets to a certain extent. Zhou et al [38] innovatively introduced the Combine Attention and Receptive Fields Block (CARFB), a module designed for receptive field feature extraction. Additionally, they incorporated the DyHead dynamic target detection head to improve the model's ability to perceive smaller objects.…”

Section: Related Work 21 Related Improved Algorithmsmentioning

confidence: 99%

SEB-YOLO: An Improved YOLOv5 Model for Remote Sensing Small Target Detection

Hui,

You,

et al. 2024

Sensors

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Due to the limited semantic information extraction with small objects and difficulty in distinguishing similar targets, it brings great challenges to target detection in remote sensing scenarios, which results in poor detection performance. This paper proposes an improved YOLOv5 remote sensing image target detection algorithm, SEB-YOLO (SPD-Conv + ECSPP + Bi-FPN + YOLOv5). Firstly, the space-to-depth (SPD) layer followed by a non-strided convolution (Conv) layer module (SPD-Conv) was used to reconstruct the backbone network, which retained the global features and reduced the feature loss. Meanwhile, the pooling module with the attention mechanism of the final layer of the backbone network was designed to help the network better identify and locate the target. Furthermore, a bidirectional feature pyramid network (Bi-FPN) with bilinear interpolation upsampling was added to improve bidirectional cross-scale connection and weighted feature fusion. Finally, the decoupled head is introduced to enhance the model convergence and solve the contradiction between the classification task and the regression task. Experimental results on NWPU VHR-10 and RSOD datasets show that the mAP of the proposed algorithm reaches 93.5% and 93.9%respectively, which is 4.0% and 5.3% higher than that of the original YOLOv5l algorithm. The proposed algorithm achieves better detection results for complex remote sensing images.

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Section: Related Work 21 Related Improved Algorithmsmentioning

confidence: 99%

SEB-YOLO: An Improved YOLOv5 Model for Remote Sensing Small Target Detection

Hui,

You,

et al. 2024

Sensors

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Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection

Ning,

Guo,

Guo

et al. 2024

IEEE Access

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Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments

Yu,

Han,

Gao

et al. 2024

JMSE

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Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas.

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Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5

Cited by 6 publications

References 32 publications

SEB-YOLO: An Improved YOLOv5 Model for Remote Sensing Small Target Detection

SEB-YOLO: An Improved YOLOv5 Model for Remote Sensing Small Target Detection

Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection

Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments

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