the reference answer GPT ᴼ ᴾ ᴱ ᴺ ᴬ ᴵ < br / > to study yolov5 algorithm robustness under shade object scene, need to acquire the following knowledge and formula:
- Basic knowledge of deep learning: yolov5 algorithm is a target detection algorithm based on deep learning technology, so it is necessary to master the basic concepts of deep learning, neural network structure and other knowledge points.
- Object detection algorithm principle: It is necessary to understand the principle of yolov5 algorithm, including input and output data format, convolutional neural network structure, loss function, etc.
- Occlusion object detection algorithm: Understand the principle and implementation methods of occlusion object detection algorithm, such as multi-scale feature fusion, image segmentation, region proposal, etc.
- Target tracking algorithm: In the detection of occluding objects, the problem of target tracking needs to be solved. Understand common target tracking algorithms, such as Kalman filter, particle filter, correlation filter, etc.
- Data enhancement techniques: Data enhancement is an important means to improve model robustness. Understand common methods of data enhancement, such as rotation, scaling, panning, flipping, etc.
- Robustness evaluation indicators: It is necessary to understand common robustness evaluation indicators, such as accuracy rate, recall rate, F1 value, etc. At the same time, it is necessary to consider the evaluation indicators under the occlusion object scene, such as the occlusion rate of visual field, etc.
- Model optimization techniques: Some model optimization techniques, such as weight initialization, regularization and pruning, can be used to solve the robustness problem in the occlusion object scene.
It should be noted that the above knowledge points are only the basis for the study on the robustness of yolov5 algorithm for occlusion object detection, and specific research should be carried out in combination with specific scenarios and problems.
