## 5 Conclusions  
A portable online dimension measurement system for boxes is required by the logistics industry to meet the challenging demands of intelligent logistics. In this work, the proposed dimension measurement system takes advantage of 3D reconstruction of box vertices to provide online dimension measurement. The system is based on laser triangulation and deep learning technology via a cross-line laser stripe cast onto the adjacent face of the box to be inspected. This method can accurately compute the 3D dimensions of boxes in adverse environmental conditions. The 2D coordinates of the key point in the laser-box images are detected using a novel end-to-end deep learning network with excellent performance. An effective optimization algorithm for structured light vision calibration was presented in which the camera intrinsic and extrinsic parameters of the device were improved by maximum likelihood estimation based on probability distribution. Experimental results show that the physical design for the proposed visual sensor is rational and the dimension measurement of box is effective. Our approach is readily applicable by future automated systems, which can integrate box targeting with the measurement method presented here. In the future, our work will continue to focus on the study of an intelligent and portable online box dimension measuring equipment system. Conceptualization, T.P., Z.Z., and D.Z. ; methodology, T.P., Z.Z., and D.Z. ; software, T.P. ; validation, T.P., Z.Z. ; formal analysis, T.P. and F.C. ; investigation, T.P. and D.Z. ; resources, Z.Z. ; data curation, T.P., F.C., and Z.Z. ; writing (original draft preparation), T.P. ; writing (review and editing), T.P., Z.Z., and D.Z. ; visualization, T.P. and Z.Z. ; supervision, Z.Z., F.C., and D.Z. ; project administration, T.P. ; funding acquisition, Z.Z. and D.Z. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Actual** & **Measured** & **Length** & **Relative Error of** & **Volume** & **Relative Error of** \\  & **Length/mm** & **Length/mm** & **Error/mm** & **Length (\%)** & **Error/m3** & **Volume (\%)** \\ \hline \multirow{3}{*}{(a)} & 250.5 & 250.3 & \(-\)0.2 & 0.079\% & \multirow{3}{*}{\(-\)0.00019} & \multirow{3}{*}{0.476\%} \\  & 350.8 & 350.1 & \(-\)0.7 & 0.199\% & & \\  & 454.6 & 453.7 & \(-\)0.9 & 0.197\% & & \\ \hline \multirow{3}{*}{(b)} & 560.5 & 561.6 & +1.1 & 0.196\% & \multirow{3}{*}{0.000130} & \multirow{3}{*}{0.125\%} \\  & 430.5 & 431.3 & +0.8 & 0.185\% & & \\  & 430.5 & 429.4 & \(-\)1.1 & 0.255\% & & \\ \hline \multirow{3}{*}{(c)} & 400.0 & 402.3 & +2.3 & 0.575\% & \multirow{3}{*}{0.000175} & \multirow{3}{*}{0.488\%} \\  & 450.0 & 452.1 & +2.1 & 0.466\% & & \\  & 200.0 & 198.9 & \(-\)1.1 & 0.550\% & & \\ \hline \multirow{3}{*}{(d)} & 520.0 & 523.6 & +3.6 & 0.690\% & \multirow{3}{*}{0.164\%} \\  & 440.0 & 438.3 & \(-\)1.7 & 0.386\% & & \\  & 515.0 & 513.6 & \(-\)2.4 & 0.466\% & & \\ \hline \multirow{3}{*}{(e)} & 220.4 & 220.0 & \(-\)0.4 & 0.181\% & \multirow{3}{*}{\(-\)0.00012} & \multirow{3}{*}{0.451\%} \\  & 300.7 & 299.4 & \(-\)1.3 & 0.432\% & & \\  & 430.5 & 431.2 & +0.7 & 0.162\% & & \\ \hline \multirow{3}{*}{(f)} & 288.0 & 288.1 & +0.1 & 0.034\% & \multirow{3}{*}{\(-\)0.00001} & \multirow{3}{*}{0.039\%} \\  & 288.0 & 287.6 & \(-\)0.4 & 0.138\% & & \\  & 310.0 & 310.2 & +0.2 & 0.064\% & & \\ \hline \multirow{3}{*}{(g)} & 1350.0 & 1354.5 & +4.5 & 0.333\% & \multirow{3}{*}{0.000450} & \multirow{3}{*}{0.673\%} \\  & 330.0 & 329.8 & \(-\)0.2 & 0.060\% & & \\ \cline{1-1}  & 150.0 & 150.6 & +0.6 & 0.400\% & & \\ \hline \multirow{3}{*}{(h)} & 1800.0 & 1807.6 & +7.6 & 0.422\% & \multirow{3}{*}{0.003262} & \multirow{3}{*}{0.503\%} \\  & 900.0 & 904.8 & +4.8 & 0.533\% & & \\ \cline{1-1}  & 400.0 & 398.2 & \(-\)1.8 & 0.450\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Working online test. **Funding:** This work was supported by the National Natural Science Foundation of China (61572307).