When embedded systems are targeted for deployment, YOLOv3-tiny, a lightweight version of YOLOv3, is usually adopted. The presented work is the first to implement a parameterised FPGA-tailored architecture specifically for YOLOv3-tiny. The architecture is optimised for latency-sensitive applications, ...
为了弥补这一缺陷,2018 年,Redmon 等人发布了 YOLO v3。这一新版本保持了 YOLO 的速度优势,提升了模型精度,尤其加强了小目标、重叠遮挡目标的识别,补齐了 YOLO 的短板,是目前速度和精度均衡的目标检测网络。” “YOLO uses sum-squared error between the predictions and the ground truth to calculate loss. The...
Their study proposed the use of a novel attention layer in conjunction with a series of state-of-the-art neural network architectures. The results indicated that the YOLO-v3 [15] model outperforms the remaining networks which were analysed with an average precision of 48.1% on their dataset. ...
The study proposes a novel deep learning-based architecture namely, Rapid-YOLO which is an extended form of YOLOv3 archi-tecture for detection of shadows. The proposed model is an extension of YOLOv3 architecture with addition of extra YOLO detection layers and convolution layers for aiding ...
this lib provide a more intuitive way to build yolov5 model architecture, you can read about it: @JiaLim98the YOLOv5 model yamls are very simple. The columns are just as shown in the comments: from which layer(s) number of times a module is repeated ...
The mean Average Precision(mAP) of the proposed model reaches 80.92%, which has a great advantage compared with Faster R-CNN. Compared with YOLOv3-Tiny, the proposed model decreases the number of parameters by 5.71 M and improves the mAP by 6.67%. Compared with YOLOv4-Tiny, the number of...
A hybrid algorithm is used to solve the activity recognition problem by combining the benefits of YOLOv3 [57] and GA. YOLOv3 is used to detect the changes in the frames using an accelerated GAs paradigm on the generated subblocks. To ensure the highest degree of accuracy, GAs parameters ...
Deep learning (DL) methodologies extract high-level features in the form of a pipeline that learns complicated patterns. The inceptionv3 [24] and VGG-16 pre-trained models with SVM are utilized for viral, bacterial, and healthy image classification [25]. AI is categorized into two sub-groups...
In the ResNet block of YOLOv3 algorithm, the output features from all the previous layers are accumulated to produce the input feature of the current layer. This requires the storage of the output feature of each of the previous layers, which is stored in SRAM as shown in Figure 3. No ...
The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, ...