Recognition of Parcel Collision and Squeeze Based on Air Pressure Feature Fusion

DUAN Chenhao; ZHU Lei; ZHANG Yuan; DU Yanping

doi:10.19554/j.cnki.1001-3563.2026.17.023

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (17) : 223-233. DOI: 10.19554/j.cnki.1001-3563.2026.17.023

Automatic and Intelligent Technology

Recognition of Parcel Collision and Squeeze Based on Air Pressure Feature Fusion

DUAN Chenhao^1a,b, ZHU Lei^1a,b*, ZHANG Yuan^1a,b, DU Yanping^1b

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Abstract

The work aims to propose a low-cost and anti-interference precise monitoring method for the force state and damage location of packages to deal with the problems of difficult traceability of package damage and the lack of an objective basis for assessing the failure of internal buffer structures in non-visual logistics transportation. A quasi-sealed chamber experimental platform that simulated real logistics packages was built, and high-frequency dynamic and low-frequency static barometric pressure sensors were symmetrically deployed inside it to collect pressure signals under typical working conditions such as single-side impact and double-side compression. Firstly, the original high-noise signal was cleaned through an adaptive algorithm based on the prior physical energy distribution. Then, the aligned multimodal pressure sequence was input into the constructed pressure-context convolutional recurrent neural network (PC-CRNN). This network utilized residual convolutions to extract spatial energy fingerprints and employed a bidirectional long short-term memory (Bi-LSTM) network to parse the temporal pulse features of multi-point impacts. Experimental results demonstrated that this system effectively solved the confusion of traditional methods in distinguishing between "bilateral continuous squeezes" and "unilateral strong impacts". The comprehensive recognition rate under complex working conditions reached 88.95%. Notably, the recognition accuracy for bilateral squeezes increased by 23.8% compared with a baseline model lacking temporal feature extraction. The dual-scale pneumatic feature fusion and spatio-temporal deep learning architecture have successfully achieved high-precision identification of package directional collision and compression, providing a low-cost, highly robust technical solution for logistics safety monitoring and multi-point force analysis.

Key words

logistics safety monitoring / dual-scale air pressure sensing / spatiotemporal feature fusion / pressure-context convolutional recurrent neural network (PC-CRNN) / bidirectional long short-term memory (Bi-LSTM)

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DUAN Chenhao, ZHU Lei, ZHANG Yuan, DU Yanping. Recognition of Parcel Collision and Squeeze Based on Air Pressure Feature Fusion[J]. Packaging Engineering. 2026, 47(17): 223-233 https://doi.org/10.19554/j.cnki.1001-3563.2026.17.023

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