Improved YOLO26 Carton Damage Detection Algorithm Based on Mobile Apple Neural Engine

TIAN Lei; ZHAN Cuili; ZHU Yi

doi:10.19554/j.cnki.1001-3563.2026.19.017

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (19) : 161-174. DOI: 10.19554/j.cnki.1001-3563.2026.19.017

Automatic and Intelligent Technology

Improved YOLO26 Carton Damage Detection Algorithm Based on Mobile Apple Neural Engine

TIAN Lei^*, ZHAN Cuili, ZHU Yi

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Abstract

Addressing the insufficient accuracy of carton damage detection in intelligent warehousing scenarios and the frequent hardware acceleration failure of the Apple Neural Engine (ANE) when lightweight algorithms are migrated to iOS devices, the work aims to propose a lightweight object detection method named YOLO26n-DCA that achieves a balance between detection accuracy and end-side performance. Firstly, an efficient channel attention module was embedded at the end of the feature fusion network of the YOLO26n architecture to enhance the model's representation capability for multi-scale damage defects. Secondly, timing analysis was performed via Xcode Instruments to identify the acceleration bottleneck of traditional attention modules on ANE, and a Zero-Reshape Dense Channel Attention (DCA) module was designed to realize native alignment between the optimized network and the underlying operators of ANE. Furthermore, a self-built dataset containing 11 600 industrial carton damage images was constructed based on public datasets from the Roboflow Universe platform and on-site sampling for experimental validation. The mAP₅₀ (mean Average Precision at IoU=0.5) of the improved model reached 0.829, up by 2.6 percentage points compared with the baseline model. When deployed on an iPhone 16 terminal equipped with the A18 chip, the end-to-end detection latency for multi-damage targets was 17.1 ms, with a real-time on-device frame rate of 58.5 frames per second (FPS). The native ANE operator mapping rate rose from 0% to 94.2%, and the inference speed was 1.77 times that of the baseline model with the Efficient Channel Attention (ECA) mechanism. The total number of model parameters was only 3.23 MB, and the quantized CoreML model occupied a mere 5.54 MB of storage space. It is concluded that the proposed method satisfies the rigid requirements of the logistics and warehousing industry for real-time performance, detection accuracy and lightweight deployment, and provides core technical support for the industrial deployment of the YOLO26n architecture on iOS edge terminals.

Key words

carton damage detection / YOLO26n / Zero-Reshape Dense Channel Attention (DCA) / Apple Neural Engine (ANE) / lightweight

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TIAN Lei, ZHAN Cuili, ZHU Yi. Improved YOLO26 Carton Damage Detection Algorithm Based on Mobile Apple Neural Engine[J]. Packaging Engineering. 2026, 47(19): 161-174 https://doi.org/10.19554/j.cnki.1001-3563.2026.19.017

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