Foreword: As the current advanced deep learning target detection algorithm YOLOv5 and v7 series algorithms, a large number of tricks have been assembled, but when dealing with some complex background problems, it is still prone to errors and omissions. The following series of articles will focus on how to improve the YOLO series of algorithms in detail. The purpose is to provide meager help and reference for those students who are engaged in scientific research who need innovation or friends who are engaged in engineering projects to achieve better results. .
Solve the problem: The original YOLOv5 model feature fusion network is PANet. Although it can better integrate the features of different scale targets than FPN, so as to improve the effect, there is still room for improvement, and there is a more advanced feature fusion network. Friends who are interested in improving the BIFPN weighted two-way feature pyramid before can follow me and read the previous blog. Now introduce a pyramid feature fusion strategy, called adaptively spatial feature fusion (ASFF), which can filter conflicting information in the airspace to suppress inconsistent features, and improve the feature fusion ability of the network for targets of different scales.
Main principles:
论文：Learning Spatial Fusion for Single-Shot Object Detectionarxiv.org/abs/1911.09516?context=cs.CV
ASFF is used to resolve inconsistencies within the feature pyramid in first-order detectors. ASFF enables the network to directly learn how to spatially filter features at other levels, keeping only useful information for combination. For a certain level of features, the features of other levels are first adjusted to the same resolution and simply integrated, and then trained to find the best fusion method. At each spatial location, features of different levels are adaptively fused together, for example: if a location carries contradictory information, these features will be filtered out, if the features of a location have more discriminative cues , these features will be enhanced. Several benefits of ASFF: (1) Since the operation of searching for the optimal fusion is