IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
Youtian Lin, Pengming Feng, Jian Guan, Wenwu Wang and, Jonathon Chambers

TL;DR
This paper introduces IENet, a novel one-stage anchor-free neural network for efficient and accurate orientation aerial object detection, utilizing a geometric transformation, feature fusion, and an improved IoU loss.
Contribution
The paper proposes a new one-stage anchor-free detector with a geometric angle representation, feature fusion via self-attention, and an efficient IoU loss for oriented object detection.
Findings
Outperforms state-of-the-art detectors in accuracy
Reduces computational complexity for orientation detection
Demonstrates effectiveness on aerial image datasets
Abstract
Object detection in aerial images is a challenging task due to the lack of visible features and variant orientation of objects. Significant progress has been made recently for predicting targets from aerial images with horizontal bounding boxes (HBBs) and oriented bounding boxes (OBBs) using two-stage detectors with region based convolutional neural networks (R-CNN), involving object localization in one stage and object classification in the other. However, the computational complexity in two-stage detectors is often high, especially for orientational object detection, due to anchor matching and using regions of interest (RoI) pooling for feature extraction. In this paper, we propose a one-stage anchor free detector for orientational object detection, namely, an interactive embranchment network (IENet), which is built upon a detector with prediction in per-pixel fashion. First, a novel…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
