Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning and it also has a certain universality.

Visual perception system is the key component of safety driving assistance and unmanned driving systems, and the perceptive performance of which will directly affect the running safety. There are various perception algorithms for the visual system, especially in recent years, deep learning algorithms are adopted with increasing popularity among them. However, the majority of existing works mainly focuses on daytime scenes with favorable illumination and weather conditions, and relies on visible light with intensity imaging equipment. This paper is based on polarization features acquired by deep learning methods, attempting to address the problems about vehicle-road environment perception in Low-Visibility Condition. Polarization is one of the essential properties of matter, here we propose a new method by studying and analyzing the transmission characteristics of the target’s polarization information. In the proposed method, the polarization features of vehicle-road environment are first acquired by a kind of three-channel polarization imaging device, then, fused together with an intensity image, and finally followed by a semantic segmentation operation by deep networks. The experimental results clearly demonstrate that the effect of perception based on polarization features via deep learning are greatly improved compared with intensity imaging, especially in low illumination and severe weather conditions. It is of great value for environmental robustness of visual perception system and further traffic safety.

Immediate and accurate detection of foreign metal objects (FMOs) in clothing products is important for guaranteeing human safety. This article proposes an online detection approach based on deep learning, which is suitable for detecting small FMOs from X-ray images of clothing packages. A conveyor belt X-ray scanning system is developed for image collection. The X-ray images are preprocessed by using the morphological erosion operation to improve the accuracy of FMOs detection. These images are then down-sampled to reduce the computation cost. Feature pyramid network (FPN) is adopted for aggregating feature maps with different resolutions, which proved to be effective for small FMOs detection. The stochastic gradient descent (SGD) is used to optimize a multitask loss. The trained model was tested offline on 200 X-ray images, which achieved precision = 0.999, recall = 0.988, F1-score = 0.993, and AP = 0.946. Compared to original Faster region-based convolutional neural networks (R-CNN), the proposed method significantly improved the performance for small FMOs detection in terms of precision and recall rate.

In view of the strict requirements of the current high-precision measurement system for stable output power of the semiconductor LD (Laser Diode), a semiconductor LD stable power drive and multi-closed-loop control system are proposed after analyzing the semiconductor laser’s P–I (Power–Current) characteristics and temperature characteristics. The system uses a microcontroller as the core control unit and realizes the stable power output control of the semiconductor laser by controlling the current, power and temperature parameters. In this system, first, the control structure model of the controlled object has been designed. Second, a controllable closed-loop constant current feedback drive circuit has been designed and a high-precision controllable constant current drive circuit of the semiconductor laser has been obtained. Furthermore, the control circuit has been designed based on the neural PI (Proportional-Integral) control model and realizes the short-term stable power output of the semiconductor LD. Finally, a closed-loop temperature control system is designed to ensure that the operating temperature of the semiconductor laser is relatively stable and a long-term stable power output is obtained. By designing the hardware and software of the control system and conducting long-term experiments in the laboratory, we found that the system can guarantee the output power within 1 W of PD (Proportional-Differential) LD, and its long-term power stability can reach 1%. This system has a certain reference significance in using semiconductor lasers for high-quality detection when there are stringent requirements for power.