[21] developed the holistically nested edge (HED) network for edge and object boundary detection through rich hierarchical representations guided by deep supervision on side responses. [PERSON] et al. [22] developed an accurate edge detector using richer convolutional features (RCF) which adopt richer convolutional features by combining all the meaningful convolutional features in a holistic manner. [PERSON] et al. [23] proposed an effective multi-stage multi-recursive-input fully convolutional networks to address neuronal boundary detection from electron microscopy images. [PERSON] et al. [24] proposed a bi-directional cascade network to encourage the learning of multi-scale representations in different layers and detect edges that are well delineated  
Figure 1: (**a**) Measurement device (**b**) and (**c**) the measured box in the distribution system. by their scales, thereby achieving a state-of-the-art result. In general, different layers in a convolutional neural network can learn different semantic levels [25]. The local texture features in the image are learned in the shallow network. The middle layer network can extract primitive features, such as shapes and lines, whereas the deep network can learn the high-level features of objects and categories in the image. HED provides an effective deep learning network with deep supervision for edge detection. Inspired by HED, a novel end-to-end-trimmed holistically nested network is designed to detect the structured edge maps for the laser-box image in the study. The calibration parameters of the visual measurement system are another problem that should be solved. The calibration of the proposed measurement system can be decomposed into the camera intrinsic parameters and the external parameters. Camera intrinsic parameters are unique for a particular camera, and many excellent camera calibration algorithms have been proposed [26; 27; 28]. The external parameters are their relative position and the orientation from the camera and the laser projector, and the excellent calibration approach can be learned from [29; 30; 31; 32; 33]. However, the noise in the calibration image data can affect the robustness and accuracy of machine vision, leading to uncertainties in our calibration parameters. These parameters are also affected by systematic errors. These issues may become an obstacle to industrial applications. Thus, for the accurate calibration of the parameters of the proposed visual system, a novel calibration method based on the maximum likelihood estimation of the probability distribution of the internal and external parameters and filtering of outliers is proposed in this paper. In this work, we propose a hand-held box dimension measurement system based on a moving coordinate system by combining laser triangulation and deep learning technology. A novel 3D measurement scheme based on key points is developed instead of complete 3D surface reconstruction. The high measurement efficiency is maximized by detecting the key points of the two adjacent face images of the box instead of all the information on the laser stripes. We performed research and related experiments on system modeling, system calibration, measurement methods, structured edge map detection, and key point detection in the proposed visual measurement system. The main contributions of this paper are summarized as follows:  
1. A hand-held visual sensor and online measurement system based on laser triangulation and deep learning technique for box dimension measurement are proposed. 2. A valid dataset of the laser-box images is created, and an effective structured edge detection and key point detection approach based on a Trimmed-HED network and straight-line processing are proposed. 3. An optimization method is proposed to achieve the robust calibration of the visual sensor. This paper is organized as follows: in Section 2, the box dimension measurement system is introduced briefly.