Search Results (3)

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds. Full article

The field-of-view (FOV) of compound eyes is an important index for performance evaluation. Most artificial compound eyes are optical, fabricated by imitating insect compound eyes with a fixed FOV that is difficult to adjust over a wide range. The compound eye is of great significance in the field of tracking high-speed moving objects. However, the tracking ability of a compound eye is often limited by its own FOV size and the reaction speed of the rudder unit matched with the compound eye, so that the compound eye cannot better adapt to tracking high-speed moving objects. Inspired by the eyes of many organisms, we propose a soft-array, surface-changing compound eye (SASCE). Taking soft aerodynamic models (SAM) as the carrier and an infrared sensor as the load, the basic model of the variable structure infrared compound eye (VSICE) is established using an array of infrared sensors on the carrier. The VSICE model is driven by air pressure to change the array surface of the infrared sensor. Then, the spatial position of each sensor and its viewing area are changed and, finally, the FOV of the compound eye is changed. Simultaneously, to validate the theory, we measured the air pressure, spatial sensor position, and the FOV of the compound eye. When compared with the current compound eye, the proposed one has a wider adjustable FOV. Full article

Blade tip clearance (BTC) measurement and active clearance control (ACC) are becoming crucial technologies in aero-engine health monitoring so as to improve the efficiency and reliability as well as to ensure timely maintenance. Eddy current sensor (ECS) offers an attractive option for BTC measurement due to its robustness, whereas current approaches have not considered two issues sufficiently. One is that BTC affects the response time of a measurement loop, the other is that ECS signal decays with increasing speed. This paper proposes a speed adjustment model (SAM) to deal with these issues in detail. SAM is trained using a nonlinear regression method from a dynamic training data set obtained by an experiment. The Levenberg–Marquardt (LM) algorithm is used to estimate SAM characteristic parameters. The quantitative relationship between the response time of ECS measurement loop and BTC, as well as the output signal and speed are obtained. A BTC measurement method (BTCMM) based on the SAM is proposed and a geometric constraint equation is constructed to assess the accuracy of BTC measurement. Experiment on a real-time BTC measurement during the running process for a micro turbojet engine is conducted to validate the BTCMM. It is desirable and significative to effectively improve BTC measurement accuracy and expand the range of applicable engine speed. Full article