This paper studies the insufficient extracted image feature in CNN basic network towards large model parameters quantity in convolutional neural network-based target detection model. First, it analyzes calculating method and parameter quantity of separable convolution and standard convolution, and processes original image through increasing sampling layer and blocking area extraction layer on Kronecker. Then, original image will be sampled with two different ratios to form image pyramid sequence and splice two-layered pyramid image in order to guarantee the same original image size. Furthermore, a better learning ability in network can be enhanced under condition without increasing networked scale through multi-scaled training methods. The experiment shows target detection algorithm in this paper cannot only be superior to traditional algorithm but it can also be superior to YOLO model and SSD model in comprehensive performance.
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask,hadoop Frontend :-python Backend:- MYSQL
₹10000 (INR)
2019