Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.

Accurate prediction of ship’s heave motion can greatly enhance the safety of offshore operation. Due to its complexity and nonlinearity, however, ship’s heave motion prediction is a difficult task to be solved. In this paper, a new method for predicting ship’s heave motion is proposed based on an improved back propagation neural network (IBPNN). To overcome the gradient saturation phenomenon of traditional BPNN, the mean square error (MSE) loss function is replaced with a cross entropy (CE) loss function in IBPNN. Meanwhile, the weights of IBPNN is regularized by $$L_2$$L2 norm to enhance the generalization ability of traditional BPNN. Finally, conjugate gradient method is adopted to train IBPNN. The IBPNN is used to predict ship’s heave motion and the prediction results prove its effectiveness.

A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into the conventional HOCBF to obtain a new type of HOCBF with faults. Secondly, in the case of a strictly required range of control inputs, a sufficient condition is proposed to satisfy the existing constraints, where the control inputs are always within the restricted range and the sufficient condition is expressed as feasibility constraints. Next, based on the quadratic programming (QP) method, the control Lyapunov function, fault HOCBF, and feasibility constraints are incorporated together to ensure that the overall feasibility, stabilization, and safety are guaranteed of the considered closed-loop system. Finally, the adaptive cruise control system is taken as an example to verify the effectiveness of the proposed method.