Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models
This paper presents a new loss function for the prediction of oriented bounding boxes, named head-tail-loss. The loss function consists in minimizing the distance between the prediction and the annotation of two key points that are representing the annotation of the object. The first point is the center point and the second is the head of the object. However, for the second point, the minimum distance between the prediction and either the head or tail of the groundtruth is used. On this way, either prediction is valid (with the head pointing to the tail or the tail pointing to the head). At the end the importance is to detect the direction of the object but not its heading. The new loss function has been evaluated on the DOTA and HRSC2016 datasets and has shown potential for elongated objects such as ships and also for other types of objects with different shapes.
Code (0)
등록된 구현이 없습니다.
Tasks
Objectobject-detectionObject DetectionOriented Object DetectionPredictionvalidSimilar Papers 제목 키워드 기반
GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection
Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions bas…
object-detectionObject DetectionOriented Object DetectionregressionThe Multiclass Score-Oriented Loss (MultiSOL) on the Simplex
In the supervised binary classification setting, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phase, thus avoiding \textit{a posteriori} t…
Binary ClassificationCategory-Aware Dynamic Label Assignment with High-Quality Oriented Proposal
Objects in aerial images are typically embedded in complex backgrounds and exhibit arbitrary orientations. When employing oriented bounding boxes (OBB) to represent arbitrary oriented objects, the periodicity of angles c…
object-detectionObject DetectionObject Detection In Aerial ImagesOriented Object Detection+1Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i.e., the model tends to classify tail clas…
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing…