YOLOLW: A Novel Lightweight Object Detection Model

doi:10.3969/j.issn.1006-2475.2024.11.014

Abstract

Abstract: In response to the growing demand for real-time mobile object detection deployment， the current YOLO backbone network falls short. Hence， this paper proposes YOLOLW， a lightweight object detection model based on the anchor frame. Firstly， it incorporates a novel lightweight decoupling header to enhance focus on classification and regression tasks and improve model accuracy. Secondly， it designs a lightweight and reparameterized network structure that achieves superior detection accuracy while maintaining its lightweight nature. Thirdly， it enhances the feature pyramid structure （FPN） by effectively integrating shallow features through dynamic convolution and cross-hierarchy association. Lastly， spatial and channel attention mechanisms are introduced to significantly boost the model’s accuracy. Experimental results validate the effectiveness of the YOLOLW model.

Key words: target detection, YOLOLW, lightweight model, attention mechanism, FPN

CLC Number:

TP391

ZHANG Yu1, 2, LI Jing1, 2, MA Ming1, 2, WANG Zhongxiang1, 2, SUN Yan1, 2. YOLOLW: A Novel Lightweight Object Detection Model[J]. Computer and Modernization, 2024, 0(11): 91-98.

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