Advances in Production Engineering & Management
Volume 21 | Number 2 | June 2026 | pp 163–177
https://doi.org/10.14743/apem2026.2.567
Industrial multi-object detection for automotive battery manufacturing using a CBAM-enhanced YOLOv5 framework
Li, Y.X.; Chen, H.; Zhou, L.
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A B S T R A C T
The mixing stage of automotive battery production requires reliable monitoring of raw material types, personnel actions, and correct tool use. However, accurate multi-scale object detection in complex scenes remains challenging because of background interference and the requirements of embedded deployment and real-time operation. This study proposes an enhanced YOLOv5 framework for industrial multi-object detection. The method adopts a dual-stage feature-enhancement strategy designed to improve robustness while limiting parameter count and computational complexity. First, the Convolutional Block Attention Module (CBAM) is embedded in the Backbone and Neck of YOLOv5 to provide multi-granularity feature enhancement and improve the detection of small objects, such as tools. Second, the conventional CIOU loss is replaced with the Focal-EIOU loss function to optimize bounding-box regression, reduce false detections across multiple target scales, and accelerate model convergence. Finally, K-means clustering is applied to target geometric features to generate specialized anchor-box parameters better suited to industrial scenarios. Experimental results on an automotive battery production-site dataset show that the improved model's mAP@0.5 increased by 2.4 % compared with the original model, reaching 95.2 %, while mAP@0.5:0.95 improved by 3.6 %, reaching 78.5 %. The proposed framework provides a lightweight and reliable solution for multi-object detection in complex industrial environments and supports the development of intelligent visual monitoring systems for smart manufacturing.
A R T I C L E I N F O
Keywords • Automotive battery manufacturing; Industrial computer vision; Multi-object detection; Real-time object detection; Deep learning; YOLOv5; Convolutional Block Attention Module (CBAM); Dual-stage Feature Enhancement; Focal-EIOU loss
Corresponding author • Li, Y.X.
Article history • Received 26 June 2025, Revised 22 June 2026, Accepted 25 June 2026
Published on-line • 29 July 2026
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