https://bbwpublisher.com/index.php/JERA <p align="justify"><em>Journal of Electronic Research and Application (JERA)</em>&nbsp;is an international, peer-reviewed and open access journal which publishes original articles, reviews, short communications, case studies and letters in the field of electronic research and application. The covered topics include, but are not limited to: automation, circuit analysis and application, electric and electronic measurement systems, electrical engineering, electronic materials, electronics and communications engineering, power systems and&nbsp;power electronics, signal processing, telecommunications engineering, wireless and mobile, and communication.</p> <p align="justify">&nbsp;</p> Bio-Byword Scientific Publishing PTY LTD en-US Journal of Electronic Research and Application 2208-3502 https://bbwpublisher.com/index.php/JERA/article/view/11676 <p>In the context of target detection under infrared conditions for drones, the common issues of high missed detection rates, low signal-to-noise ratio, and blurred edge features for small targets are prevalent. To address these challenges, this paper proposes an improved detection algorithm based on YOLOv11n. First, a Dynamic Multi-Scale Feature Fusion and Adaptive Weighting approach is employed to design an Adaptive Focused Diffusion Pyramid Network (AFDPN), which enhances the feature expression and transmission capability of shallow small targets, thereby reducing the loss of detailed information. Then, combined with an Edge Enhancement (EE) module, the model improves the extraction of infrared small target edge features through low-frequency suppression and high-frequency enhancement strategies. Experimental results on the publicly available HIT-UAV dataset show that the improved model achieves a 3.8% increase in average detection accuracy and a 3.0% improvement in recall rate compared to YOLOv11n, with a computational cost of only 9.1 GFLOPS. In comparison experiments, the detection accuracy and model size balance achieved the optimal solution, meeting the lightweight deployment requirements for drone-based systems. This method provides a high-precision, lightweight solution for small target detection in drone-based infrared imagery.</p> Jiale Wang Copyright (c) 2025 Author(s) 2025-10-31 2025-10-31 9 6 1 6 10.26689/jera.v9i6.11676 https://bbwpublisher.com/index.php/JERA/article/view/12055 <p>The study presents AW-HRNet, a lightweight high-resolution crack segmentation network that couples Adaptive residual enhancement (AREM) in the spatial domain with Wavelet-based decomposition–reconstruction (WDRM) in the frequency domain. AREM introduces a learnable channel-wise scaling after standard 3 × 3 convolution and merges it through a residual path to stabilize crack-sensitive responses while suppressing noise. WDRM performs DWT to decouple LL/LH/HL/HH sub-bands, conducts lightweight cross-band fusion, and applies IDWT to restore detail-enhanced features, unifying global topology and boundary sharpness without deformable offsets. Integrated into a high-resolution backbone with auxiliary deep supervision, AW-HRNet attains 79.07% mIoU on CrackSeg9k with only 1.24M parameters and 0.73 GFLOPs, offering an excellent accuracy–efficiency trade-off and strong robustness for real-world deployment.</p> Dewang Ma Tong Lu Copyright (c) 2025 Author(s) 2025-11-03 2025-11-03 9 6 7 17 10.26689/jera.v9i5.12055