Real-time Detection Algorithm for Infrared Dynamic Targets Based on YOLO-IDOD

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Real-time Detection Algorithm for Infrared Dynamic Targets Based on YOLO-IDOD

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[1]ZHAO Xin,FEI Xiaohu,WANG Dongyu,et al.Real-time Detection Algorithm for Infrared Dynamic Targets Based on YOLO-IDOD[J].Journal of Zhengzhou University (Engineering Science),2026,47(5):93-101.[doi:10.13705/j.issn.1671-6833.2026.05.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 5 Page number: 93-101 Column: Public date: 2026-09-09

Title:: Real-time Detection Algorithm for Infrared Dynamic Targets Based on YOLO-IDOD

Author(s):: ZHAO Xin^1,2, FEI Xiaohu¹, WANG Dongyu¹, HAN Shoufei¹; 1. School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China;2. State Key Laboratory of Digital and Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China

Keywords:: infrared dynamic target detection; YOLOv12; DAM; CACONV; multi-dimensional channel attention mechanism

CLC:: TP391. 41TN219

DOI:: 10.13705/j.issn.1671-6833.2026.05.001

Abstract:: To overcome the limitations of existing infrared object detection algorithms, which mainly arise from inadequate exploitation of temporal information and inter‑frame dependencies in dynamic target detection, thereby resulting in suboptimal detection accuracy, a real‑time infrared dynamic object detection framework based on YOLO‑IDOD, incorporating a dynamic attention module（DAM）and a channel attention convolution（CACONV）module, were proposed. The YOLOv12s architecture was employed as the baseline network, in which a dynamic attention mechanism was integrated at the input stage to extract short‑term optical flow features via an optical flow network, effectively suppressing background motion interference and enhancing the network’s sensitivity to target motion characteristics. Furthermore, a channel attention convolution module was embedded within the network architecture, where channel‑wise attention mechanisms was introduced at both the input and output stages to facilitate more discriminative feature representation and selection for the DAM‑enhanced features. The proposed modules was designed as plug‑and‑play components, enabling spatiotemporal feature aggregation and adaptive feature selection, thereby improving the generalization capability of the network for infrared dynamic target detection. Experimental evaluations demonstrated that the improved YOLO‑IDOD model achieved a precision of 79.9 %, a recall of 62.5 %, an mAP@50 of 77.7 %, and an mAP@95 of 57.3 % on a mixed dataset composed of a self‑constructed dataset（IRDA）and the public FLIR_ADAS_v2 dataset. Compared with the baseline YOLOv12s model, precision, mAP@50, and mAP@95 were improved by 5.2 percentage points, 4.6 percentage points, and 2.4 percentage points, respectively, while maintaining a comparable recall rate, thereby effectively enhancing detection accuracy and generalization performance for infrared dynamic targets.

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Last Update: 2026-09-07