Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

Zhou, Qikai; Zhang, Wei; Li, Ruizhi; Wang, Jin; Zhen, Shuhui; Niu, Fu

doi:10.1117/1.jei.31.4.043049

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…In the NWPU VHR-10 dataset, compared to Refs. 32 and 13, LRSNet shows a slightly lower mAP of 0.5%/0.2%. However, its Params(M) and GFLOPs are only 53.2%/52.7% and 21.0%/21% of theirs, enabling deployment on devices with more limited hardware resources.…”

Section: Experimental Results and Analysismentioning

confidence: 90%

“…YOLOV3-Tiny, YOLOV4-Tiny, and YOLOv5n belong to the YOLO series, representing different versions of lightweight general-purpose object detection models. References 28–32 and 13, on the other hand, denote lightweight networks specifically designed for remote sensing object detection. In addition, boldfaced and italicized values in the tables indicate the optimal and suboptimal results, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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LRSNet: a high-efficiency lightweight model for object detection in remote sensing

Zhu,

Miao,

Wang

2024

J. Appl. Rem. Sens.

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Unmanned aerial vehicles (UAVs) exhibit the ability to flexibly conduct aerial remotesensing imaging. By employing deep learning object-detection algorithms, they efficiently perceive objects, finding widespread application in various practical engineering tasks. Consequently, UAV-based remote sensing object detection technology holds considerable research value. However, the background of UAV remote sensing images is often complex, with varying shooting angles and heights leading to difficulties in unifying target scales and features. Moreover, there is the challenge of numerous densely distributed small targets. In addition, UAVs face significant limitations in terms of hardware resources. Against this background, we propose a lightweight remote sensing object detection network (LRSNet) model based on YOLOv5s. In the backbone of LRSNet, the lightweight network MobileNetV3 is used to substantially reduce the model's computational complexity and parameter count. In the model's neck, a multiscale feature pyramid network named CM-FPN is introduced to enhance the detection capability of small objects. CM-FPN comprises two key components: C3EGhost, based on GhostNet and efficient channel attention modules, and the multiscale feature fusion channel attention mechanism (MFFC). C3EGhost, serving as CM-FPN's primary feature extraction module, possesses lower computational complexity and fewer parameters, as well as effectively reducing background interference. MFFC, as the feature fusion node of CM-FPN, can adaptively weight the fusion of shallow and deep features, acquiring more effective details and semantic information for object detection. LRSNet, evaluated on the NWPU VHR-10, DOTA V1.0, and VisDrone-2019 datasets, achieved mean average precision of 94.0%, 71.9%, and 35.6%, with Giga floating-point operations per second and Param (M) measuring only 5.8 and 4.1, respectively. This outcome affirms the efficiency of LRSNet in UAV-based remote-sensing object detection tasks.

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Section: Experimental Results and Analysismentioning

confidence: 90%

Section: Experimental Results and Analysismentioning

confidence: 99%

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

Zhu,

Miao,

Wang

2024

J. Appl. Rem. Sens.

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“…Lang Lei [13] et al based on YOLOX-Tiny and relied on variable convolution to lightweight the remote sensing target detection model. Zhou Qi Kai [14] et al introduced the weighted two-way pyramid into YOLOv5s, which improved the accuracy of ship classification.…”

Section: Introductionmentioning

confidence: 99%

The ship classification and detection method of optical remote sensing image based on improved YOLOv7-tiny

Cheng,

Yuan,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity.

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“…Low-quality images captured in severe weather conditions (e.g., dust, haze, and smoke) usually suffer from the problems of color shift and low contrast, which limit the performance of many computer vision algorithms, such as object tracking, 1 – 3 object detection, 4 , 5 and segmentation 6 , 7 . Therefore, it is necessary to study the single-image dehazing algorithm to improve the robustness of the computer vision algorithms.…”

Section: Introductionmentioning

confidence: 99%

Retinex decomposition and fusion dehazing network

et al. 2023

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Image dehazing has been widely used in vision-based fields, such as detection, segmentation, traffic monitoring, and automated vehicle system. However, most of the existing endto-end dehazing networks are fully data driven without physical constraints or prior information guidance, leading to difficulties in exploring latent structures and statistical characteristics of hazy images. We propose a novel Retinex decomposition-fusion dehazing network consisting of a dual-branch decomposition module and a fusion optimization module. Different from existing solutions, we decompose the clear images in the commonly used RESIDE dataset based on Retinex theory to construct the clear illumination map and reflection map datasets to drive the network training, equivalently imposing reasonable constraints on the network and achieving impressive dehazing performances. The dual-branch decomposition module is developed to estimate the illumination map and the reflection map, respectively. The illumination map mainly contains the global features of the image, whereas the reflection map reflects the inherent color properties of the image and contains rich details, with which we explore the latent structures and statistical characteristics of hazy images. In addition, the dual-branch structure avoids the error accumulation and information cancellation existing in current methods. Subsequently, the estimated illumination map and reflection map are fused and refined via the fusion optimization module to access the dehazed image. Experiments show that the proposed network has better generalization and visual effects than existing fully data-driven methods and can be applied successfully to real-world scenarios.

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Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

Cited by 14 publications

References 13 publications

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

LRSNet: a high-efficiency lightweight model for object detection in remote sensing

The ship classification and detection method of optical remote sensing image based on improved YOLOv7-tiny

Retinex decomposition and fusion dehazing network

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