Lightweight target detection for the field flat jujube based on improved YOLOv5

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have limitations in capturing long-range dependencies. DMs require large prior models and computationally intensive denoising steps. Transformers have powerful modeling capabilities but face challenges due to quadratic complexity with input image size. To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks. We utilize a Unet architecture to stack our proposed Omni Selective Scan (OSS) blocks, consisting of an OSS module and an Efficient Feed-Forward Network (EFFN). Our proposed omni selective scan mechanism overcomes the unidirectional modeling limitation of SSMs by efficiently modeling image information flows in all six directions. Furthermore, we conducted a comprehensive evaluation of our VmambaIR across multiple image restoration tasks, including image deraining, single image super-resolution, and real-world image super-resolution. Extensive experimental results demonstrate that our proposed VmambaIR achieves state-of-the-art (SOTA) performance with much fewer computational resources and parameters. Our research highlights the potential of state space models as promising alternatives to the transformer and CNN architectures in serving as foundational frameworks for next-generation low-level visual tasks.

pdf/vmambair-visual-state-space-model-for-image-restoration-4mrun418wf.pdf

VmambaIR: Visual State Space Model for Image Restoration

We put forward and present a demonstration of an actively controlled optical beam steering optical wireless communication (OWC) system based on an integrated actively steerable optical phased array (OPA). We theoretically and experimentally analyze the emitted optical beam full-width-half-maximum (FWHM) divergence angle and field-of-view (FOV) of the proposed integrated actively steerable OPA. Experimental results show that the beam steering can be achieved over a range of 17° with a minimum optical beam FWHM divergence angle of 1.49°. Besides, the integrated OPA chip design, beam forming optimization, packaging, as well as temperature stabilization are also discussed. Genetic Algorithm (GA) is utilized to optimize the applied voltages for the 30 phase shifters to produce low-divergence optical beams in different directions. A proof-of-concept bit-error-rate (BER) measurement using 10 Gbit/s on-off-keying (OOK) signal is also carried out, and the beam steered optical signal satisfying the pre-forward error correction BER limit (pre-FEC BER = 3.8 × 10 −3 ) is achieved.

Actively Controllable Beam Steering Optical Wireless Communication (OWC) Using Integrated Optical Phased Array (OPA)

The accurate detection of pineapples of different maturity levels in a complex field environment is the key step to achieving the early yield estimation and mechanized picking of pineapple. This study proposes a target detection model based on the improved YOLOv7 to achieve the accurate detection and maturity classification of pineapples in the field. First, the attention mechanism SimAM is inserted into the structure of the original YOLOv7 network to improve the feature extraction ability of the model. Then, the max-pooling convolution (MPConv) structure is improved to reduce the feature loss in the downsampling process. Finally, the non-maximum suppression (NMS) algorithm is replaced by the soft-NMS algorithm, with a better effect at improving the detection effect when pineapple is in occlusion or overlaps. According to the test, the mean average precision (mAP) and recall of the model proposed in this paper are 95.82% and 89.83%, which are 2.71% and 3.41% higher than those of the original YOLOv7, respectively. The maturity classification accuracy of the model and the detection performance under six different field scenarios were analyzed quantitatively. This method provides an effective scheme for the vision system of the field pineapple picking robot.

/pdf/a-pineapple-target-detection-method-in-a-field-environment-2fenh57p.pdf

A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7

With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

/pdf/deep-learning-for-clothing-style-recognition-using-yolov5-1wlrx2j6.pdf

Deep Learning for Clothing Style Recognition Using YOLOv5

Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking and address the problem of low illumination of images collected at night, an enhancement algorithm is proposed to enhance the images, which are recognized. We compare the actual detection results of the five enhancement algorithms, i.e., histogram equalization, Laplace transform, gamma transform, logarithmic variation, and dark channel enhancement processing under the different numbers of fruits, periods, and video tests. The results show that combined with dark channel enhancement, YOLOv5 has the highest recognition rate. Finally, the experimental results demonstrate that YOLOv5 is better than SSD, DSSD, and EfficientDet in terms of recognition accuracy, and the correct rate can reach more than 90%. Meanwhile, the method has good robustness in complex environments such as partial occlusion and multiple fruits.

/pdf/strawberry-maturity-recognition-algorithm-combining-dark-2jwijrz5.pdf

Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

https://www.mdpi.com/2304-6732/9/1/30/pdf?version=1641288873

Machine Learning Applications for Short Reach Optical Communication

Optical wireless communications (OWC) explore the broad unregulated optical spectrum to provide high speed wireless communications, which are promising in beyond 5G. We will discuss the fundamentals and recent developments of short-range OWC systems in this tutorial.

Evolution of Short-Range Optical Wireless Communications

The optical wireless communication (OWC) technology explores the broad unregulated optical spectrum to provide high-speed wireless communications, and the visible, ultraviolet and near-infrared wavelength ranges all have been investigated. Compared with the conventional radio frequency (RF) band, the spectrum in OWC systems is much less congested and the interference is much lower. The OWC technology also provides enhanced physical layer security due to the natural confinement of optical beams that makes it difficult to intercept the transmitted signal. Hence, the OWC technology has been widely studied and considered as a promising candidate in future beyond-5G communications. The OWC technology has been considered for both long-range and short-range applications, and in this paper we will introduce the fundamentals and recent developments of short-range OWC systems. In particular, we will focus on the key short-range OWC applications in indoor personal or local area communications, underwater wireless communications, wireless data center networks, and vehicular communications. We will also introduce some recent widely studied advanced techniques to boost the performance of short-range OWC systems, including the spatial domain diversity, multiplexing and modulation principles to improve the system robustness and data rate, and the machine learning algorithms and hardware accelerators to suppress both linear and nonlinear effects to improve OWC signal quality and bit-error-rate performance.

Terahertz and optical wireless communications (OWC) technologies have been considered as promising enablers that have potential to contribute equally to the realization of a sixth-generation (6G) wireless future. Experimental demonstrations show that, although inherently different, both technology types can support multi-kilometer Tbit/s wireless data transmission, which is a key goal set by almost all 6G roadmaps. Although both technology types can contribute equally to the realization of 6G roadmaps, to date, the development of these technologies for communications and non-communications applications has been ongoing independently. This is mainly because the distant similarity between both technology types masks their competitive performance. This article complements efforts made by academia and industry to explore the potentials and challenges of terahertz and OWC technologies as key enablers for 6G applications. Specifically, we weigh up the pros and cons of both technology types based on selected key metrics. The purpose of these comparisons is to highlight application areas in which strengths of these technologies could potentially be integrated.

Optical and Terahertz Wireless Technologies: the Race to 6G Communications

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Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

Machine Learning Applications for Short Reach Optical Communication

Evolution of Short-Range Optical Wireless Communications

Evolution of Short-Range Optical Wireless Communications

Optical and Terahertz Wireless Technologies: the Race to 6G Communications