An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes

Ni, Jianjun; Shen, Kang; Chen, Yan; Yang, Simon X.

doi:10.1109/tim.2023.3244819

Cited by 28 publications

(8 citation statements)

References 69 publications

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“…Thirdly, we will look for datasets with large standard deviation changes to reveal the effect of training set standard deviation changes on the predictive ability of the model. In addition, Zhu et al [77] obtained impressive results by employing a transfer learning model to predict DO in Lake Taihu. Chen et al [78], utilizing a BPNN model, successfully predicted the total suspended solids concentration in Poyang Lake with an R 2 of 0.89, demonstrating excellent transferability.…”

Section: Limitations and Perspectivementioning

confidence: 99%

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Chen,

Yao,

Zhu

et al. 2024

Water

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Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

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Section: Limitations and Perspectivementioning

confidence: 99%

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Chen,

Yao,

Zhu

et al. 2024

Water

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“…One‐stage detectors directly regard object location and classification as a regression problem, without the proposed generation steps of the region. The most representative methods are SSD (Ni et al, 2023; Wang, Wang, et al, 2022) and YOLO (Qin et al, 2022; Vajgl et al, 2022; Zhao & Zhu, 2023) families, which perform multi‐scale prediction of targets to narrow the differences between scale distributions. Inspired by a single‐shot multi‐box detector (SSD), SSD‐Like (Ni et al, 2023) proposed an improved dual‐threshold non‐maximum suppression (DT‐NMS) algorithm to alleviate the brightness and occlusion problems caused by complex indoor environments.…”

Section: Related Workmentioning

confidence: 99%

“…The most representative methods are SSD (Ni et al, 2023; Wang, Wang, et al, 2022) and YOLO (Qin et al, 2022; Vajgl et al, 2022; Zhao & Zhu, 2023) families, which perform multi‐scale prediction of targets to narrow the differences between scale distributions. Inspired by a single‐shot multi‐box detector (SSD), SSD‐Like (Ni et al, 2023) proposed an improved dual‐threshold non‐maximum suppression (DT‐NMS) algorithm to alleviate the brightness and occlusion problems caused by complex indoor environments. SLMS‐SSD (Wang, Wang, et al, 2022) designed a self‐learning multi‐scale object detection network by balancing semantic information and spatial information.…”

Section: Related Workmentioning

confidence: 99%

A military reconnaissance network for small‐scale open‐scene camouflaged people detection

Liu

2023

Expert Systems

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Although the work of identifying animal and plant objects with highly similar patterns (e.g., texture, intensity, colour, etc.) to the background has recently attracted more research interest but rarely involves the military complex environment. Design an efficient camouflage small‐scale object detection algorithm that is capable of quickly discriminating the objects in open scenes from a long distance, to pre‐empt the enemy. In this work, we first recognize the fact that existing open‐source training datasets are scarce, and we have created a specific disguised people benchmark covering multiple scenes and weather conditions. Second, because the severe corruption of camouflage capabilities and chaotic scenes in the open battlefield on detailed features and generalization intensifies the challenge of feature extraction from a long‐range perspective, we propose a novel end‐to‐end Small‐scale open scene Camouflage Object Detection Network, called SM‐CODN. Inspired by the characteristics of biological brain partition, a multi‐domain partition module (MPM) with domain‐decoupling is proposed to enable specific knowledge learning for samples with obvious discrepancies in camouflage domain distribution. Concurrent with our work, we have designed a multi‐scale fusion module (MFM) to strengthen the semantic features related to small‐scale disguised objects. Moreover, due to the convergence direction of the detector in reasoning being inconsistent, a feature separation enhancement module (FSEM) is also proposed. Experimental results show that SM‐CODN surpasses many classic object detection methods and shows strong competitiveness compared with state‐of‐the‐art ones.

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“…Numerous studies have demonstrated that two-stage networks are appropriate for applications with higher detection accuracy requirements [14,15]. One-stage networks, such as SSD [16,17] and Yolo [18,19], directly generate class probabilities and coordinate positions and are faster than two-stage networks. Therefore, one-stage networks have great advantages in UAV practical applications with high-speed requirements.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Object Detection for UAVs Based on Deep Learning

Tang,

Ni,

Zhao

et al. 2023

Remote Sensing

Self Cite

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With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.

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An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes

Cited by 28 publications

References 69 publications

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

A military reconnaissance network for small‐scale open‐scene camouflaged people detection

A Survey of Object Detection for UAVs Based on Deep Learning

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