Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Shen, Chao; Ma, Caiwen; Gao, Wei

doi:10.3390/s23031261

Cited by 10 publications

(5 citation statements)

References 29 publications

(31 reference statements)

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“…Concurrently, Andrze et al [37] integrated spiral search grasshopper optimization for better feature selection, addressing data imbalance and overfitting. Further, multiple researchers [38]- [41] have significantly advanced small target detection, crucially enhancing the identification of small, densely packed targets in satellite imageries.…”

Section: A Detection Of Small Targets In Satellite Remote Sensing Ima...mentioning

confidence: 99%

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Zhao,

Guo,

Shao

et al. 2024

IEEE Access

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Satellite remote sensing technology significantly contributes to intelligent transportation by optimizing traffic planning via global perspectives and rich data, enhancing traffic efficiency and reducing environmental impact. However, current target detection models frequently exhibit low accuracy in vehicle detection tasks due to complex background interference in satellite imageries and a need for critical semantic information. To improve vehicle target detection accuracy, this study introduces SatDetX-YOLO, a vehicle detection model for satellite remote sensing images based on YOLOv8. The model involves reconstructing the backbone network with FasterNet for enhanced feature extraction, a redesigned decoupled head for improved computational efficiency and complex data processing, and incorporating the Deformable Attention Module (DAM) to increase sensitivity to small targets and feature correlation capture. Employing the Maximum Probabilistic Distance IoU (MPDIoU) loss function enhances adaptability and generalization to diverse vehicle targets. Experimental results demonstrate that under comparable FPS, SatDetX-YOLO's Precision (P), Recall (R), and Mean Average Precision (mAP) improved by 3.5%, 3.3%, and 3.2%, respectively. Despite a minor reduction in FPS, the model significantly enhances detection accuracy, striking a balance between accuracy and speed.

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Section: A Detection Of Small Targets In Satellite Remote Sensing Ima...mentioning

confidence: 99%

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Zhao,

Guo,

Shao

et al. 2024

IEEE Access

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“…Zhu et al [35] merged a transformer-based prediction head with the YOLOv5 detection model, achieving remarkable performance improvements in large-scale variations and high-density contexts. To recognize highlevel semantic information and enhance the perception of local geometric features, Multiple Attention Mechanism Enhanced YOLOX (MAME-YOLOX) [36] integrates the Swin transformer into the neck module of YOLOX. Due to the computationally intensive nature of the internal attention mechanisms in transformers, prior efforts have employed it solely as a localized enhancement module within CNNbased frameworks, thereby underutilizing its intrinsic global-aware capabilities.…”

Section: Object Detection In Uav Imagesmentioning

confidence: 99%

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Feng,

Wang,

Zhang

et al. 2024

CMC

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Transformer-based models have facilitated significant advances in object detection. However, their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle (UAV) imagery. Addressing these limitations, we propose a hybrid transformer-based detector, H-DETR, and enhance it for dense small objects, leading to an accurate and efficient model. Firstly, we introduce a hybrid transformer encoder, which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently. Furthermore, we propose two novel strategies to enhance detection performance without incurring additional inference computation. Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression. Adversarial denoising learning is a novel enhancement method inspired by adversarial learning, which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise. Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach, achieving a significant improvement in accuracy with a reduction in computational complexity. Our method achieves 31.9% and 21.1% AP on the VisDrone and UAVDT datasets, respectively, and has a faster inference speed, making it a competitive model in UAV image object detection.

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“…The YOLOX model has improved and optimised based on the YOLO v5 network model from three aspects: decoupled head layer, data enhancement and anchor-free. Additionally, the model has been widely used due to its high accuracy and efficiency [25,26]. Unlike Faster R-CNN, YOLO can predict several candidate frames simultaneously.…”

Section: Walnut Shell-kernel Recognition Algorithm Based On Yoloxmentioning

confidence: 99%

“…YOLOX uses the overall layout of the YOLO series, and its network structure mainly comprises Input, Backbone network, Neck network and Prediction [25,26]. Figure 3 shows the structure of YOLOX.…”

Section: Yolox Network Structurementioning

confidence: 99%

Machine Vision-Based Chinese Walnut Shell–Kernel Recognition and Separation

Zhang,

Wang,

Liu

et al. 2023

Applied Sciences

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Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.

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Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Cited by 10 publications

References 29 publications

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Machine Vision-Based Chinese Walnut Shell–Kernel Recognition and Separation

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