LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

Huang, Zhanchao; Li, Wei; Xia, Xiang-Gen; Wang, Hao; Jie, Feiran; Tao, Ran

doi:10.1109/tgrs.2021.3067470

Cited by 56 publications

(30 citation statements)

References 38 publications

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“…However, the computational workload of these two detectors is still too large to meet the capacity of our target embedded device (i.e., to achieve a real-time speed of 60 FPS, the network model of the detector should have less than 8 GFLOPs workload). The only lightweight detector that can compete with the proposed scheme in terms of the number of parameters is LO-Det [ 56 ]. However, in LO-Det, the authors have designed a very complex FPN network, in which channel shuffle and split operations were repeatedly used in each layer.…”

Section: Resultsmentioning

confidence: 99%

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Lang

Zhang

et al. 2021

Sensors

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Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.

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Section: Resultsmentioning

confidence: 99%

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Lang

Zhang

et al. 2021

Sensors

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“…Furthermore, the proposed GGHL is used to replace the label assignment strategy of other mainstream AOOD methods to evaluate its versatility. Besides, the lightweight AOOD model LO-Det [11] is improved by the proposed GGHL, and its performance is evaluated on embedded platforms to verify the application friendliness. Third, comparative experiments on several public datasets of different scenes are evaluated to compare the performance of the proposed GGHL with the state-of-the-art methods.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Benefiting from the open-source AOOD datasets annotated with OBBs in the scenes like remote sensing [5], the prediction of the OD model has become more refined, which helps to accurately locate the object in the image and reflect its shape and direction. In the AOOD task, whether the two-stage methods [10,20,21] or the one-stage methods [7,11,12], most of them adopt the anchor-box-based framework due to its mature application in various OD tasks. However, since oriented anchors are more prone to mismatch problems and have more hyperparameters than horizontal anchors, many works have dealt with them.…”

Section: A Arbitrary-oriented Object Detectionmentioning

confidence: 99%

“…It means that one needs to spend a lot of efforts adjusting the hyperparameters when the dataset is changed [14]. Moreover, these easily overlooked recessive costs cannot be reduced by a lightweight CNN model [11]. To solve the problem that the anchor-based strategy relies on many hyperparameters and may have mismatches, some OD methods, such as FCOS [3] and CenterNet [15], designed different anchor-free assignment strategies, as shown in Fig.…”

Section: B Label Assignment Strategymentioning

confidence: 99%

“…Most of the AOOD methods, such as SCRDet [10], LO-Det [11], DAL [12], etc., use the anchor-based label assignment strategy, as shown in Fig. 2 (a).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Huang,

Li,

Xia

et al. 2021

Preprint

Self Cite

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Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Labeling (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-boundingbox (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.

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Real-time detection algorithm for non-motorized vehicles based on D-YOLO model

Ding

Hu³

et al. 2023

Multimed Tools Appl

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LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

Cited by 56 publications

References 38 publications

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Real-time detection algorithm for non-motorized vehicles based on D-YOLO model

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