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Found 6 papers, 3 papers with code
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity.
Multi-Grained Angle Representation for Remote Sensing Object Detection
The existing AOOD methods face the challenges of ambiguity and high costs in angle representation.
Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
Specifically, sampling positions of the localization convolution in TS-Conv are supervised by the oriented bounding box (OBB) prediction associated with spatial coordinates, while sampling positions and convolutional kernel of the classification convolution are designed to be adaptively adjusted according to different orientations for improving the orientation robustness of features.
MPANet: Multi-Patch Attention For Infrared Small Target object Detection
To this end, a multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed.
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
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.
Ranked #31 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection
Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper.
