Remote sensing image object detection is fundamental to the automatic interpretation of aerial and satellite imagery. It aims to identify objects of predefined categories from given images and regress the localization information for each object. Remote sensing image object detection technology has broad applications in practical scenarios，such as intelligent surveillance，urban planning，port management，and military surveys. In contrast to natural images，where objects are typically horizontally aligned and backgrounds are relatively simple，remote sensing images are captured from a top-down perspective using spaceborne or airborne sensors，offering a much broader field of view. Consequently，objects in remote sensing images are often distributed in arbitrary orientations，and the background information is more complex and diverse. Although some horizontal object detectors perform well on natural images，horizontal bounding boxes （HBB） cannot describe the orientation of objects and often introduce unnecessary background redundancy. Moreover，in densely arranged scenes，the overlap between HBBs and neighboring boxes can be substantial，which may lead to missed or false detections. By contrast，oriented bounding boxes （OBB） can represent objects more accurately and are better suited for object detection in dense scenes. However，during regression，the skew intersection over union （SkewIoU） is difficult to compute and nondifferentiable，preventing the model from optimizing its predicted OBB parameters via backpropagation. To address this，current mainstream methods convert oriented bounding boxes into 2D Gaussian distribution and compute the distance between these distributions as the regression loss. This approach approximates the calculation of SkewIoU while remaining differentiable，and such methods are collectively referred to as Gaussian distance loss （GDL）. However，given the periodic nature of angles，GDL-based methods exhibit discontinuities in angle regression. Furthermore，they suffer from imbalanced angle perception across targets with varying aspect ratios. The interplay between these issues considerably degrades the detection performance of the model. In this study，a detection algorithm named joint angle continuous representation and perceptual balance （JCPB） is proposed to mitigate the mentioned challenges.

To address the angle boundary discontinuity problem in oriented object detection，this study proposes an angular vector decomposition method （AVDM），which decomposes angle parameters into an angle vector representation. To eliminate discontinuous regions inherent in traditional angular representations，this vector representation is further extended to encompass the entire unit circle plane，ensuring continuous variation of the angular vector during OBB regression. Aiming at the periodic discrepancy issue in angle loss calculation between rectangular and square targets，this study introduces an innovative shape aware coefficient method. Without increasing the encoding length of the angular vector，a shape-aware coefficient dynamically adjusts its value on the basis of the target’s aspect ratio，thereby modulating the change period of the angular loss function. To address the problem of imbalanced angle perception，we proposed the angle perceptual balance strategy，which adaptively calculates loss weighting coefficients based on the aspect ratio of targets and adjusts their application timing according to the SkewIoU value. This enhances the model’s angle perception capability for low-aspect-ratio targets during the later stages of training. Finally，on the basis of a 2D Gaussian distribution representation method，we further enhance the model’s detection performance by joint angle continuous representation and perceptual balance.

This study conducted comparative experiments using FCOS and RetinaNet detectors on the DOTA，DIOR-R，and HRSC2016 datasets to validate the effectiveness of the proposed methods. Furthermore，comparisons with current mainstream models on the DOTA dataset demonstrate the superiority of the proposed model. Evaluation metrics include average precision at 75% （AP₇₅） intersection over union threshold，mean average precision （mAP），and parameters. On the DOTA dataset，incorporating AVDM improves AP₇₅ and mAP by 2.47% and 1.03% on average compared with the baseline model. Further incorporating APBS resulted in additional average improvements of 0.84% and 0.43% for AP₇₅ and mAP，respectively. On the DIOR dataset，integrating AVDM achieved average gains of 1.37% and 0.95% for AP₇₅ and mAP. Further incorporating APBS resulted in additional improvements of 0.82% and 0.36% for AP₇₅ and mAP，respectively. On the HRSC dataset，the AVDM method achieved average gains of 5.63% and 3.86% for AP₇₅ and mAP，respectively. After incorporating APBS，AP₇₅ and mAP improved by 0.44% and 0.49% on average. Experimental results across multiple datasets demonstrate that APBS effectively enhances the model’s angular learning capability for small aspect ratio targets，while AVDM achieves continuous angular representation，effectively resolving boundary discontinuity issues. Compared with other encoding methods，AVDM introduces only 0.04 M additional parameters，achieving continuous angular representation and periodic adaptation to angular losses for rectangular and square targets at minimal parametric cost. Experiments on the HRSC dataset show that detection accuracy for slender objects is highly susceptible to boundary discontinuity issues，while angle perceptual imbalance has a relatively weaker impact. Finally，comprehensive comparisons with current mainstream remote sensing image orientation object detection methods on the DOTA dataset demonstrate that our approach achieves an mAP of 73.78%，outperforming other methods. Moreover，it achieves optimal accuracy on small vehicles，ships，tennis courts，storage tanks，and roundabouts categories. Additionally，when using Swin-Transformer as the backbone network，the model’s performance is further improved，reaching an mAP of 74.71%.

Extensive experimental results demonstrate that the proposed AVDM achieves continuous angular representation，while APBS effectively enhances the model’s perceptual capability for small aspect ratio targets. Building upon this foundation，JCPB remarkably improves the overall performance of the detection model in complex remote sensing scenarios by integrating continuous angular representation with perceptual balance.