Object detection of VisDrone by stronger feature extraction FasterRCNN

Zhang, Xiangxiang; Wang, Chun-Yuan; Jin, Jie; Huang, Li

doi:10.1117/1.jei.32.1.013018

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…This approach enhances the network’s ability to detect targets by combining local, global, and coordinate information. Zhang et al [ 27 ] proposed a feature extractor that, through the integration of high- and low-resolution feature maps, generated a comprehensive semantic feature map. Cai et al [ 28 ] introduced a novel feature fusion module that effectively combines the semantic information from the lower layer with the positional information from the upper layer.…”

Section: Related Workmentioning

confidence: 99%

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection

Tang,

Yun,

Chen

et al. 2024

Sensors

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Object detection is a crucial component of the perception system in autonomous driving. However, the road scene presents a highly intricate environment where the visibility and characteristics of traffic targets are susceptible to attenuation and loss due to various complex road scenarios such as lighting conditions, weather conditions, time of day, background elements, and traffic density. Nevertheless, the current object detection network must exhibit more learning capabilities when detecting such targets. This also exacerbates the loss of features during the feature extraction and fusion process, significantly compromising the network’s detection performance on traffic targets. This paper presents a novel methodology by which to overcome the concerns above, namely HRYNet. Firstly, a dual fusion gradual pyramid structure (DFGPN) is introduced, which employs a two-stage gradient fusion strategy to enhance the generation of more comprehensive multi-scale high-level semantic information, strengthen the interconnection between non-adjacent feature layers, and reduce the information gap that exists between them. HRYNet introduces an anti-interference feature extraction module, the residual multi-head self-attention mechanism (RMA). RMA enhances the target information by implementing a characteristic channel weighting policy, thereby reducing background interference and improving the attention capability of the network. Finally, the detection performance of HRYNet was evaluated by utilizing three datasets: the horizontally collected dataset BDD1000K, the UAV high-altitude dataset Visdrone, and a custom dataset. Experimental results demonstrate that HRYNet achieves a higher mAP_0.5 compared with YOLOv8s on the three datasets, with increases of 10.8%, 16.7%, and 5.5%, respectively. To optimize HRYNet for mobile devices, this study presents Lightweight HRYNet (LHRYNet), which effectively reduces the number of model parameters by 2 million. The results demonstrate that LHRYNet outperforms YOLOv8s in terms of mAP_0.5, with improvements of 6.7%, 10.9%, and 2.5% observed on the three datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection

Tang,

Yun,

Chen

et al. 2024

Sensors

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“…Object detection is one of the most important and challenging branches in the field of computer vision, which has a wide application. [23][24][25][26][27] In the field of CNN-based computer vision, the design of network models can be broadly classified into one-stage and two-phase. One-stage models, such as SSD, 28 RetinaNet, 29 and YOLO series, [30][31][32][33][34][35] are fast.…”

Section: Related Workmentioning

confidence: 99%

HA-DQS-Net: dynamic query design based on transformer with hollow attention

Wang,

Yan,

et al. 2024

J. Electron. Imag.

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A common problem in the field of object detection is that the image features could not be fully expressed. And another issue is that the static query selection in the detection transformer (DETR)-like models cannot adapt well to different datasets due to the fixed number of selected object queries. To solve these problems, hollow attention (HA) and dynamic query selection (DQS) modules were proposed, and a network HA-DQS-Net was further formed. HA integrates specially designed masks into self-attention to better combine channel and spatial directional feature information, thereby learning more complex and comprehensive target features. DQS improves the idea of static query selection in the current DETR-like model by dynamically selecting the number of object queries based on the actual number of targets in the image, which enhances the accuracy of the model. HA-DQS-Net, which combines the advantages of HA and DQS, has a competitive performance in the field of object detection. The excellent detection effectiveness of our viewpoint is validated based on PASVAL VOC and a homemade smoking dataset. It is worth noting that all APs have been improved when HA is applied to different DETR-like models, which improves the universality of the HA module.

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“…Traditional target detection methods, such as sliding windows and manual feature extraction, are exemplified by techniques like Haar [3], HOG [4], Hu moment [5], SIFT [6], SURF [7], and DPM [8]. The evolution of computer vision and deep learning has ushered target detection into agricultural production prominence, with algorithms bifurcated into single-stage (e.g., YOLO series [9][10][11], SSD series [12][13][14], RetinaNet series [15,16]) and two-stage detection algorithms (e.g., RCNN series [17], FasterRCNN series [18]). Apple target detection, melding computer vision and agriculture, automates apple identification and localization in images.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

Han,

Lu,

Dong

et al. 2024

Applied Sciences

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This study addresses the challenges in the non-destructive detection of diseased apples, specifically the high complexity and poor real-time performance of the classification model for detecting diseased fruits in apple grading. Research is conducted on a lightweight model for apple defect recognition, and an improved VEW-YOLOv8n method is proposed. The backbone network incorporates a lightweight, re-parameterization VanillaC2f module, reducing both complexity and the number of parameters, and it employs an extended activation function to enhance the model’s nonlinear expression capability. In the neck network, an Efficient-Neck lightweight structure, developed using the lightweight modules and augmented with a channel shuffling strategy, decreases the computational load while ensuring comprehensive feature information fusion. The model’s robustness and generalization ability are further enhanced by employing the WIoU bounding box loss function, evaluating the quality of anchor frames using outlier metrics, and incorporating a dynamically updated gradient gain assignment strategy. Experimental results indicate that the improved model surpasses the YOLOv8n model, achieving a 2.7% increase in average accuracy, a 24.3% reduction in parameters, a 28.0% decrease in computational volume, and an 8.5% improvement in inference speed. This technology offers a novel, effective method for the non-destructive detection of diseased fruits in apple grading working procedures.

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Object detection of VisDrone by stronger feature extraction FasterRCNN

Cited by 3 publications

References 49 publications

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection

HRYNet: A Highly Robust YOLO Network for Complex Road Traffic Object Detection

HA-DQS-Net: dynamic query design based on transformer with hollow attention

Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

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