As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deplo...
详细信息
As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospe
Research in cancer care increasingly focuses on survivorship issues, e.g. managing disease- and treatment-related morbidity and mortality occurring during and after treatment. This necessitates innovative approaches t...
详细信息
Research in cancer care increasingly focuses on survivorship issues, e.g. managing disease- and treatment-related morbidity and mortality occurring during and after treatment. This necessitates innovative approaches that consider treatment side effects in addition to tumor cure. Current treatment-planning methods rely on constrained iterative optimization of dose distributions as a surrogate for health outcomes. The goal of this study was to develop a generally applicable method to directly optimize projected health outcomes. We developed an outcome-based objective function to guide selection of the number, angle, and relative fluence weight of photon and proton radiotherapy beams in a sample of ten prostate-cancer patients by optimizing the projected health outcome. We tested whether outcome-optimized radiotherapy (OORT) improved the projected longitudinal outcome compared to dose-optimized radiotherapy (DORT) first for a statistically significant majority of patients, then for each individual patient. We assessed whether the results were influenced by the selection of treatment modality, late-risk model, or host factors. The results of this study revealed that OORT was superior to DORT. Namely, OORT maintained or improved the projected health outcome of photon- and proton-therapy treatment plans for all ten patients compared to DORT. Furthermore, the results were qualitatively similar across three treatment modalities, six late-risk models, and 10 patients. The major finding of this work was that it is feasible to directly optimize the longitudinal (i.e. long- and short-term) health outcomes associated with the total (i.e. therapeutic and stray) absorbed dose in all of the tissues (i.e. healthy and diseased) in individual patients. This approach enables consideration of arbitrary treatment factors, host factors, health endpoints, and times of relevance to cancer survivorship. It also provides a simpler, more direct approach to realizing the full beneficial potenti
Fruit loading and packaging are still labor-intensive tasks during postharvest commercialization, of which the key issues is to realize the real-time detection and adjustment of fruit posture. However, fruit stem/caly...
详细信息
Fruit loading and packaging are still labor-intensive tasks during postharvest commercialization, of which the key issues is to realize the real-time detection and adjustment of fruit posture. However, fruit stem/calyx position is a key structural characteristic for fruit posture and will also affect fruit internal quality detection. In this paper, an image acquisition system based on fruit posture adjustment equipment was set up, and the YOLO-v5 algorithm based on deep learning was used to study the real-time recognition of stem/calyx of apples. First, hyperparameters were determined, and the training method of transfer learning was used to obtain better detection performance;then the networks with different widths and depths were trained to find the best baseline detection net;finally, the YOLO-v5 algorithm was optimized for this task by using detection head searching, layer pruning and channel pruning. The results showed that under the same setting conditions, YOLO-v5s had a more superior usability and could be selected as the baseline network considering detection performance, model weight size, and detection speed. After optimization, the complexity of the algorithm was further reduced. The model parameters and weight volume were decreased by about 71 %, while mean Average Precision (mAP) and F1-score (F1) were only decreased by 1.57 % and 2.52 %, respectively. The optimized algorithm could achieve real-time detection under CPU condition at a speed of 25.51 frames per second (FPS). In comparison with other deep learning target detection algorithms, the algorithm used in this paper was similar to other lightweight networks in complexity. Its mAP and F1 were 0.880 and 0.851, respectively. This was better than other one-stage object detection algorithms in detection ability, only lower than that of Faster R-CNN. The optimized YOLO-v5s achieved 93.89 % accuracy in fruit stem/calyx detection for different cultivars of apples. This research could lay the foundation f
To quickly and accurately detect defects in Akidzuki ( Pyrus pyrifolia Nakai) pears after harvest, this study aims to develop a method for Akidzuki pear defect detection based on computer vision and deep learning mode...
详细信息
To quickly and accurately detect defects in Akidzuki ( Pyrus pyrifolia Nakai) pears after harvest, this study aims to develop a method for Akidzuki pear defect detection based on computer vision and deep learning models. It mainly consists of obtaining high-quality images using an image acquisition system and proposing a new YOLOAP detection model to identify defects in Akidzuki pears. The model uses YOLOv5 as the main architecture. The GhostDynamicConv (GDC) module is obtained by replacing the standard convolution in the Ghost module with a dynamic convolution. The C3-GhostDynamicConv (C3-GDC) module is obtained by replacing the Bottleneck module of C3 in Neck with the GDC module, simplifying the network while improving the model's accuracy. Meanwhile, Bottleneck Attention Module (BAM) is introduced after C3-GDC to refine the intermediate features. In addition, the original bounding box loss function is replaced with Wise-IoUv3 (WIoUv3) to accelerate the model convergence. The results demonstrate that YOLO-AP performs better in Akidzuki pear defect detection, with a mAP@0.5 of 0.939, a recall of 0.921, and a detection speed of 454.5 fps (2.2 ms per image). These values represent a 4.2 %, 3.5 %, and 1.4 % improvement over the baseline model. Comparing YOLO-AP with the updated YOLOv9 and other detection models, YOLO-AP is more accurate and faster. These findings demonstrated that the proposed method can detect Akidzuki pear defects in real time, efficiently and accurately, providing technical support for post-harvest defect detection.
We present a novel approach for post-mapping optimization. We exploit the concept of generalised matching, a technique that finds symbolically all possible matching assignments of library cells to a multi-output netwo...
详细信息
ISBN:
(纸本)9780818682001
We present a novel approach for post-mapping optimization. We exploit the concept of generalised matching, a technique that finds symbolically all possible matching assignments of library cells to a multi-output network specified by a Boolean relation. Several objectives are targeted: area minimization under delay constraints; power minimization under delay constraints; and unconstrained delay minimization. We describe the theory of generalized matching and the algorithmic optimization required for its efficient and robust implementation. A tool based on generalized matching has been implemented and tested on large examples of the MCNC'91 benchmark suite. We obtain sizable improvements in: speed (6% in average, up to 20.7%); area under speed constraints (13.7% an average, up to 29.5%); and power under speed constraints (22.3% in average, up to 38.1%).
暂无评论