# IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection Source: [IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:04:46.235Z Authors: Youtian Lin, Pengming Feng, Jian Guan, Wenwu Wang, Jonathon Chambers Published: 2019-12-02T18:02:30.000Z ## 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 geometric transformation is employed to better represent the oriented object in angle prediction, then a branch interactive module with a self-attention mechanism is developed to fuse features from classification and box regression branches. Finally, we introduce an enhanced intersection over union (IoU) loss for OBB detection, which is computationally more efficient than regular polygon IoU. Experiments conducted demonstrate the effectiveness and the superiority of our proposed method, as compared with state-of-the-art detectors. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on DOTA](): Object Detection; DOTA; mAP: 57.14. - [Object Detection on DOTA 1.0 (test)](): Object Detection; DOTA 1.0 (test); Plane AP: 80.2. - [Oriented Object Detection on DOTA v1.0 (test)](): Oriented Object Detection; DOTA v1.0 (test); SV: 49.71. ## Original sources and implementation [Original paper]() [PDF]()