International Journal of Innovative Computing and Applications (3 papers in press)

Regular Issues

• Wireless Inertial Motion Capture System and Pose Trajectory Fusion Algorithm for Animation and Film Design  
  by Shifeng Wang 
  Abstract: The film and animation industry increasingly demand real-time performance and accuracy. Traditional optical motion capture methods face limitations due to environmental lighting and equipment constraints, often failing to meet high production standards. To address this issue, this study proposes a motion capture system that integrates an error-state Kalman filter and improved adaptive damped least squares. The system fuses data from multiple inertial measurement unit sensors through the error-state Kalman filter and optimises pose trajectories with the improved adaptive damped least squares, significantly enhancing motion capture performance. Experimental results show that the proposed system achieves a pose estimation error converging to 1.5 degrees within 2.5 s, and the trajectories closely match the optical ground truth. During dynamic tasks, the system exhibited absolute orientation errors of 2.44
  Keywords: Motion capture system; Pose trajectory fusion algorithm; Inertial measurement unit; Kalman filter; Animation and film design.
  DOI: 10.1504/IJICA.2026.10078298
   
  
• A Framework for Indoor Visual Element Extraction Using an Improved Attention Generative Adversarial Network  
  by Miao Nie, Zhongping Sun 
  Abstract: Traditional generative adversarial networks (GAN) suffer from insufficient structural modelling and loss of details in indoor visual element extraction. This study proposes an improved attention GAN model framework that integrates spatial attention mechanisms and channel attention allocation mechanisms. This framework guides the generator to continuously optimise the feature expression process through the supervised feedback mechanism of the discriminator, thereby improving the accuracy and completeness of visual element extraction, ensuring feature recognition and reconstruction in complex image scenes. The experimental outcomes showed that the algorithms element extraction accuracy was 93.5%, the edge segmentation accuracy was 91.2%, and it exhibited stronger robustness in interference environments. The indoor element extraction model achieved an element extraction accuracy of over 90.3% on different datasets, demonstrating good convergence and generalisation ability. The structural similarity of the extracted element feature images reached 0.94, with a PSNR of 37.4 dB. The above results indicate that the improved attention GAN model can effectively identify various elements in complex indoor scenes and maintain a high degree of detail restoration in indoor visual element extraction. This study provides more reliable technical support for indoor environment understanding and intelligent design.
  Keywords: Indoor visual element extraction; Generate adversarial networks; Attention mechanism; Spatial attention; Channel attention.
  DOI: 10.1504/IJICA.2026.10078660
   
  
• Intelligent inspection and monitoring of minor defects in complex slopes using unmanned aerial vehicles based on improved YOLOv11-PEW architecture  
  by Yong Lu, Li Tian, Sheng Yuan 
  Abstract: To address the issues of missed detection of small targets, interference from complex backgrounds, and blurred boundaries in visual inspection of geological disasters, this paper proposes the YOLOv11-PEW model: it introduces a P2 microscopic inspection head to retain shallow features to reduce texture loss, embeds an efficient multi-scale attention (EMA) module to calibrate feature weights across space to suppress background noise, and uses the Wise-IoU (WIoU) loss function (LF) to optimize boundary regression accuracy. Experimental findings demonstrate that YOLOv11-PEW achieves a mAP@0.5 of 90.1% on the self-built slope dataset, a 5.8% improvement over the baseline (YOLOv11n). Furthermore, while ensuring compliance with the real-time inference standard for embedded edge devices (>30 FPS), it significantly improves the detection accuracy of extremely small targets (Area <32 x 32) by 14.6%. Visualisation analysis (Grad-CAM) further confirms that the model can accurately focus on the disease itself from diffuse background noise, demonstrating excellent robustness.
  Keywords: highway slope protection; detection of minor defects; YOLOv11; attention mechanism; unmanned aerial vehicle remote sensing.
  DOI: 10.1504/IJICA.2026.10078678