An Improved Method for Small Target Recognition Based on Faster RCNN

Liu, Qunpo; Wang, Qi-jing; Hanajima, Naohiko; Su, Bo

doi:10.1007/978-981-16-6324-6_32

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The formula for calculating ψya is as follows: (23) When the dimension of the eigenvector satisfies t >>d, the main eigenvector for image target recognition can be obtained by converting into (τ T yaψya)d×d, where τ T yaψya is a diagonal matrix. If the difference between the non-similar features of the image target is obvious, assuming that the eigenvector matrix of the orthogonal matrix is represented by W, then:…”

Section: Feature Fusion Methods For Fast Target Recognitionmentioning

confidence: 99%

“…In order to improve the accuracy and real-time performance of the model in image target detection and recognition, a target feature matching module is used in the existing R-CNN network model and a feature map close to the same target is obtained by calculating the similarity of the features extracted from the model. In view of low accuracy of small target recognition, a small target recognition method based on improved Faster_Rcnn is proposed in reference [23]. As the ROI pool method in Faster Rcnn may lead to quantization error in operation, inaccurate positioning and other problems in detection, an improved ROI alignment method is adopted to eliminate quantization error.…”

Section: Introductionmentioning

confidence: 99%

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Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Sun¹,

Kuang²,

Zhang³

2022

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Under the natural state of unrestricted conditions, the accuracy of effective recognition of image target information captured by ordinary cameras is significantly reduced. At present, the mainstream research methods for image target recognition focus on the processing of images based on algorithms with strong ability to describe image target features so as to improve the image target recognition performance in cases of complex noise interference. However, most of the methods cannot adapt to various changes in the background when the environment changes. Therefore, this article conducts studies on the fast target recognition method based on multi-scale fusion and deep learning. The optimized local binary pattern algorithm and the HOG algorithm are used to extract the image target features, the dimension reduction of the extracted image target features is carried out based on the generalized discriminant analysis algorithm, and multi-scale fusion of image targets is accomplished based on the discriminant correlation analysis. The experimental results verify the effectiveness of the proposed algorithm.

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Section: Feature Fusion Methods For Fast Target Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Sun¹,

Kuang²,

Zhang³

2022

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“…QueryDet is applied in FCOS and Faster-R CNN and is tested on the COCO dataset and the visDrone dataset for small objects, achieving better results than the original algorithms in terms of accuracy and inference speed [42]. Liu et al addressed the problem of low accuracy in small target recognition by changing the ROI alignment method, which reduced the quantization error of Faster R-CNN and improved its accuracy by 7% compared to the original model [43].…”

Section: Object Detectionmentioning

confidence: 99%

Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

et al. 2023

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In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection of small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge due to the limited pixel values and interference from background noise. To address this challenge, this paper proposes an integrated object detection approach that utilizes an improved YOLOv5 model for real-time detection of small water surface floaters. The proposed improved YOLOv5 model effectively detects small objects by better integrating shallow and deep features and addressing the issue of missed detections and, therefore, aligns with the characteristics of the water surface floater dataset. Our proposed model has demonstrated significant improvements in detecting small water surface floaters when compared to previous studies. Specifically, the average precision (AP), recall (R), and frames per second (FPS) of our model achieved 86.3%, 79.4%, and 92%, respectively. Furthermore, when compared to the original YOLOv5 model, our model exhibits a notable increase in both AP and R, with improvements of 5% and 6.1%, respectively. As such, the proposed improved YOLOv5 model is well-suited for the real-time detection of small objects on the water’s surface. Therefore, this method will be essential for large-scale, high-precision, and intelligent water surface floater monitoring.

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An Improved Method for Small Target Recognition Based on Faster RCNN

Cited by 2 publications

References 4 publications

Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Soft-NMS-Enabled YOLOv5 with SIOU for Small Water Surface Floater Detection in UAV-Captured Images

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