Deformable 1D Directional Convolution with Bidirectional Offsets for Oriented Object Detection.
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| Title: | Deformable 1D Directional Convolution with Bidirectional Offsets for Oriented Object Detection. |
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| Authors: | Li, Ying1 (AUTHOR), Li, Xuemei1 (AUTHOR) xmli@sdu.edu.cn, Zhang, Caiming1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p934. 17p. |
| Subjects: | Signal convolution, Convolutional neural networks, Object recognition (Computer vision), Image processing, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? A deformable 1D directional convolution is proposed to implement rotated 1D convolution for adaptively extracting the features of oriented objects. A tri-branch convolution layer is desinged by combining the deformable 1D directional convolution with the standard square-shaped convolution in a parallel manner. An orientation-aware feature pyramid network is presented by integrating the tri-branch convolution layer with the feature pyramid network. What are the implications of the main findings? The proposed deformable 1D directional convolution only requires simple bidirectional offsets to efficiently implement a rotated 1D convolution, avoiding the time-consuming rotation operation. The oriented features of objects can be effectively extracted by the orientation-aware feature pyramid network. The performance of oriented object detection can be improved by adopting the orientation-aware feature pyramid network. Oriented object detection is an important and challenging task in the field of image processing and computer vision. The main challenge in detecting oriented objects comes from their high aspect ratio and being distributed with arbitrary orientations. Various methods have been developed to handle this issue. However, most existing works rely on time-consuming rotation and interpolation operations to align the feature representations of oriented objects. To avoid these operations, in this paper, we first introduce a simple yet effective deformable 1D directional convolution (D1DD-Conv), which implements a rotated convolution by deforming the 1D convolution kernel with horizontal and vertical offsets. Based upon this directional convolution, we then design a tri-branch convolution layer and integrate D1DD-Conv into the feature pyramid network for extracting the directional features of objects. Furthermore, we present a deep model to deal with the oriented object detection task. By allowing the offsets only along with horizontal and vertical directions, D1DD-Conv essentially corresponds to a rotated 1D convolution but without any rotation operations. This simple design is beneficial for efficiently capturing the orientation features of different oriented objects, leading to accurate prediction of the oriented bounding box of each oriented object. Some experiments on three popular datasets show that our model can achieve superior detection performance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A deformable 1D directional convolution is proposed to implement rotated 1D convolution for adaptively extracting the features of oriented objects. A tri-branch convolution layer is desinged by combining the deformable 1D directional convolution with the standard square-shaped convolution in a parallel manner. An orientation-aware feature pyramid network is presented by integrating the tri-branch convolution layer with the feature pyramid network. What are the implications of the main findings? The proposed deformable 1D directional convolution only requires simple bidirectional offsets to efficiently implement a rotated 1D convolution, avoiding the time-consuming rotation operation. The oriented features of objects can be effectively extracted by the orientation-aware feature pyramid network. The performance of oriented object detection can be improved by adopting the orientation-aware feature pyramid network. Oriented object detection is an important and challenging task in the field of image processing and computer vision. The main challenge in detecting oriented objects comes from their high aspect ratio and being distributed with arbitrary orientations. Various methods have been developed to handle this issue. However, most existing works rely on time-consuming rotation and interpolation operations to align the feature representations of oriented objects. To avoid these operations, in this paper, we first introduce a simple yet effective deformable 1D directional convolution (D1DD-Conv), which implements a rotated convolution by deforming the 1D convolution kernel with horizontal and vertical offsets. Based upon this directional convolution, we then design a tri-branch convolution layer and integrate D1DD-Conv into the feature pyramid network for extracting the directional features of objects. Furthermore, we present a deep model to deal with the oriented object detection task. By allowing the offsets only along with horizontal and vertical directions, D1DD-Conv essentially corresponds to a rotated 1D convolution but without any rotation operations. This simple design is beneficial for efficiently capturing the orientation features of different oriented objects, leading to accurate prediction of the oriented bounding box of each oriented object. Some experiments on three popular datasets show that our model can achieve superior detection performance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060934 |
