In the field of optoelectronic display, quantum dot (QD) display has attracted intensive attention due to its wide spectral adjustability, narrow full width at half maxima (FWHM), high color purity and other excellent properties, which is considered to be an outstanding candidate for the next generation of display technology. The performance of quantum dots light-emitting diodes (QLEDs) has been rapidly improved, and especially the red, green and blue cadmium-based QLEDs all show comparable luminescent performance to organic light emitting diodes (OLED). However, cadmium is a seriously hazard to human body and environment and violates sustainable development path. Therefore, researchers pay more attention to environment-friendly quantum dot materials, among which InP quantum dot with the outstanding properties such as large Bohr radius, wide spectral adjustability and so on has become the most promising environment-friendly material to replace cadmium-based quantum dots. In this work, the recent progress about the synthesis and photoluminescence performance of InP quantum dots (core-shell structure, ligand selection, etc.) and the luminescent performance of InP QLED are summarized, and the current problems and challenges as well as the feasible solutions in InP QLED are discussed and outlooked.

Manganese (Mn2+) ion-doped all-inorganic CsPbCl3 perovskite quantum dots have shown great potential for application in Mini/Micro-LED display devices due to their unique photoluminescence properties and nano-scale particle size. However, the current photoluminescence quantum yields of CsPbCl3∶Mn2+ perovskite quantum dots is low and cannot meet the needs of practical applications. Herein, the potassium (K+) and Mn2+ ions co-doped CsPbCl3 perovskite quantum dots are synthesized via the hot injection method. The prepared CsPbCl3∶(K+, Mn2+) perovskite quantum dots present a dual-color emission, which can be assigned to the perovskite excitons emission and Mn2+ ions emission. The photoluminescence quantum yield of CsPbCl3∶(K+, Mn2+) perovskite quantum dots is improved from 16.5% of original to 66.2% through the optimization of K+ ions doping concentration. Meanwhile, the regulation mechanism of K+ ions doping on the photoluminescence properties of CsPbCl3∶Mn2+perovskite quantum dots are investigated. It is shown that the incorporation of K+ ions can effectively inhibit the formation of intrinsic defect states and Mn-Mn dimers or Mn-related defects in CsPbCl3∶(K+, Mn2+) perovskite quantum dots, thus enhancing the radiative recombination luminescence of carriers in perovskite quantum dots. Benefiting from the strategy of hetero-ions doping, these doped perovskite quantum dots are expected to be applied in Mini/Micro-LED display fields.

Polymer dispersed liquid crystal (PDLC) is a thin film formed by liquid crystal (LC) droplets embedded in a continuous polymer matrix in the form of micron-sized droplets. The use of click chemistry method to prepare PDLC can avoid the problems of dye decomposition caused by traditional photopolymerization-induced phase separation. In this study, four polyethylene glycol diacrylates (PEGDA) of different alkyl chain lengths were used as raw materials to synthesize PDLC films through thiol-ene click reaction and thiol-isocyanate coupling reaction, to study the effects of different alkyl chain length olefin monomers on the electro-optical properties of PDLC films. The results showed that as the chain length of olefin alkyl chain in the system increased, the threshold voltage (Vth) , saturation voltage (Vsat) and contrast ratio (CR) decreased, while off-state transmittance (Toff) and on-state transmittance (Ton) increased. By selecting olefin with appropriate alkyl chain length, PDLC films with good electro-optical properties can be prepared.

The liquid crystal optical phase shifter applied in the mid-infrared band is prepared. The sapphire with high transmittance in the mid-infrared band is selected as the substrate, the ultra-thin indium tin oxide (ITO) film is used as the transparent electrode, and the mid-infrared liquid crystal with low absorption is selected as a medium. By applying voltage to the liquid crystal phase shifter, the liquid crystal molecules form a periodic phase distribution under the action of the electro-optic effect, thereby realizing the phase modulation of the mid-infrared laser. The 20 μm liquid crystal optical phase shifter prepared in this paper can achieve a phase modulation depth of 2.59 π under the irradiation of 39 μm mid-infrared laser. When the effective value of the switching voltage changes from 2.65 V to 337 V, the response time is 19 ms, which can achieve the fast response of the mid-infrared liquid crystal optical phase shifter under the condition of high cell thickness . Finally, the functional requirements of liquid crystal phase shifter in mid-infrared band can be realized.

Organic electroluminescent display (OLED) is considered to be the development direction of future display due to its advantages of low power consumption, self-luminescence, high flexibility, rich colors and fast response speed. With the introduction of technologies such as piezoelectric inkjet printing, such as non-contact, precise positioning of droplets, and large-area fast printing, mass production applications of large-size OLEDs have become possible. The uniformity of droplets in the printing and jetting process plays a vital role in the formation of high-quality OLED functional layers. For this reason, this paper reviews the performance optimization methods of piezoelectric printing OLED, mainly from the optimization of pulse waveform, self-healing control system and rheological characteristics of the ink to further improve the droplet injection performance, and further cited the application of piezoelectric printing OLED in the display and lighting fields.

Image sticking is an important factor affecting the quality of liquid crystal display (LCD) panel. The improvement of poor image sticking has been a continuous research topic in LCD panel factory. The degree of image sticking is positively correlated with the content of impurity ions. In order to monitor the content of impurity ions in the LTPS(Low Temperature Poly-Silicon)-LCD cell, the measurement system of the LTPS-LCD panel test cell is systematically studied, and the stability and resolution of the measurement system are discussed. Firstly, the measurement system designes a new test cell for the LTPS technology panel, and then uses voltage holding residual (VHR) to evaluate the impurity ion content in the cell, and the measurement systems analysis (MSA) is used to evaluate the reliability. Finally, the resolution of the measurement system is tested by comparing the changes of VHR under different ultraviolet (UV) irradiation time and photo-alignment baking time. The experimental results show that the measurement system gage repeatability, reproducibility(Gage R&R%)is 10.77%, P/T is 26.93% 5, the measurement results are reliable and stable, and can effectively identify VHR differences. The test cell designed in this paper meets the requirements of the LTPS-LCD panel industry for impurity ion content monitoring, and provides important guidance for monitoring factory impurity ions in actual production.

A Mura defect detection method based on effective background reconstruction and contrast enhancement is proposed. Firstly, a new background reconstruction method based on defect region pre-elimination is proposed, which can effectively reconstruct the background image and eliminate the interference of uneven brightness. Then, the dual-γ piecewise exponential transform method based on Otsu is introduced to enhance the difference image, which can effectively solve the problem of background residual and better enhance the contrast and contour of Mura region. Finally, the Mura defects can be separated quickly and accurately by using the dynamic threshold segmentation method. The experimental results show that, compared with the traditional polynomial surface fitting method and the discrete cosine transform method, the detection effect of this method for various types of Mura defects is stable, and the detection rate and no false alarm rate are more than 97%.

In order to solve the problems of image brightness loss and gray-level jump in low grayscale conditions of LED display screens, a low grayscale image enhancement processing algorithm for LED display screens using dither matrix is proposed. According to the principle of LED display and the image enhancement principle of traditional dithering algorithm, the concept of “decimal gray level” of LED display is proposed, and then the “decimal gray level” data that enhances the display effect of each pixel is obtained according to related methods such as testing. The dither matrix realizes its corresponding “decimal gray level” in accordance with the “principle of minimum error” in 4 frames of images. The sub-pixel dithering technology is used to further reduce the flicker caused by the image jitter to the human eyes. The algorithm was compared and simulated with Matlab software to verify the feasibility and effectiveness of the algorithm. The experimental results show that the algorithm has a significant improvement effect on the uneven transition of the low gray level of the LED display screen, and the gray level display ability of the gray level below 10 is increased to 4 times of the original. At the same time, the minimum controllable gray level brightness becomes 15%~25% of the original, which improves the contrast of the LED display.

Aiming at the problem that brackets occlude the bottom image of a 720° panoramic camera, a method for eliminating the occlusion of the bottom bracket based on step-by-step shooting is proposed. The bottom occlusion is eliminated by shooting the side and top once and shooting twice on the bottom. On this basis, the inconsistency of image brightness are optimized on the side, top, and bottom. The global uniformity algorithm can cause images in different regions to influence each other, causing deviations in the uniformity results.Therefore, a Wallis uniform color algorithm based on region growing method is proposed for image segmentation.The experimental results show that the method in this paper can eliminate the bottom image occlusion. Using the algorithm in this paper to even color, the image color difference is reduced by 59.3% compared with the global algorithm.

In order to solve the problem of dark color and incomplete defogging after image defogging, an image defogging algorithm based on multi-scale concat convolutional neural network is proposed in this paper. Taking the foggy image as the input, the shallow layer information of the image is extracted from the single scale convolution layer through the preprocessing module, and then the multi-scale mapping module is designed to realize the depth feature learning and the fusion of the deep and shallow layer features. The deconvolution module is used to restore the image size, and the coarse transmittance map corresponding to the foggy image is obtained through the convolution operation. Finally, the haze free image is restored according to the atmospheric scattering model. The experimental results show that the proposed method is superior to other algorithms in both synthetic and natural foggy images, and the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) can reach 29.238 and 0.950, respectively. The proposed algorithm can effectively avoid the dark color and distortion of the image, improve the image defogging performance and show good visual effect.

Aiming at the problem of large scene, large field of view image applications and the slow speed of traditional image stitching algorithms, a fast image stitching algorithm based on offline calibration is adopted. In practical applications, the positions of multiple cameras are fixed, and offline calibration is used to calculate the homography matrix of image-stitching, which saves a lot of feature extraction and registration time. On the basis of fast image alignment, a fast image fusion algorithm based on the Laplacian pyramid of YUV color space is proposed in this paper, and it has better fusion effect and faster speed than the traditional Laplacian pyramid fusion algorithm. Experimental results show that the image fusion speed of the article algorithm is three times that of traditional Laplacian image fusion. The alorithm of this paper can meet the real-time image stitching speed requirements in application scenarios, with good image stitching effects.

An improved scheme based on feature fusion for the lightweight object detection algorithm deployed in embedded devices is proposed in this paper. The algorithm is based on the MobileNetV2-SSD lightweight object detection algorithm with the idea of FPN feature fusion. It combines the three feature layers of MobileNetV2-SSD that contain more semantic information, and is regenerated based on the fused feature layer Feature pyramid for object detection. The improved model and the original model are compared and tested on the PASCAL VOC data set. Compared with the original model, the mAP is increased by 3.6%, and reaches 76.5%. At the same time, the detection effect of small targets is significantly improved. Finally, it is tested in the embedded device Jetson AGX Xavier. The improved model has a detection speed close to the original model when the network structure is more complex, reaching the detection speed of 22FPS, which can realize real-time object detection on embedded devices.

To improve the precision and recall rate of fire smoke detection model in multi-scene fire smoke detection applications, and avoid the tedious manual smoke feature extraction process, a fire smoke detection model is proposed which is based on convolutional neural network YOLOv4. In the last layer of the backbone network, four different scales of maximum pooling are added: 13×13, 9×9, 5×5, and 1×1. The multi-scale feature fusion uses PANet (Path Aggregation Network) to improve network feature extraction capabilities. In addition, a channel attention network is added to the network prediction head to enhance the ability of the YOLO Head to extract effective smoke information. For the fire smoke data set, the size of the candidate frame is clustered using the K-means algorithm to get a size closer to the fire smoke data set. Due to the identification of smoke, the loss function is simplified, the classification error is eliminated, and the algorithm converges faster. Data enhancement methods such as image flipping and random erasure are used in the training phase to reduce the risk of overfitting. Experimental results show that the fire smoke detection model has excellent performance. Its precision can reach 92.5%, Recall can reach 87.7%, and the detection speed can reach 5.1 frames/s, which improves the performance of fire smoke detection model in multi-scene fire smoke detection applications.

In an intelligent transportation system, the vehicle detection is an important prerequisite for vehicle tracking and identification. However, the traditional vehicle detection algorithms cannot effectively maintain a balance between accuracy and real-time performance. In this paper, a new moving vehicle detection algorithm based on improved adaptive pixel segmentation is proposed. The initial background model is established based on multi frame interval image, and an evaluation method of background region change is proposed based on the spatio-temporal variation degree. Based on this, an adaptive updating strategy of learning rate is formulated. By setting a trust interval, whether the current background model needs to be updated can be adaptively determined according to the current traffic conditions and pixels whether are in the trust interval, thereby the accurate and fast detection of moving vehicles are realized. The performance indicators of Recall, Precision and F-measure of the improved adaptive pixel segmentation algorithm in different scenarios are 0.929, 0.864 and 0.888, respectively, which are higher than the traditional adaptive pixel segmentation algorithm, and the processing time of the algorithm is 88.37 ms, nearly 10 ms faster than the traditional adaptive pixel segmentation algorithm. It basically meets the requirements of high speed, high precision and high robustness of vehicle detection.