5 Significant Object Detection Challenges and Solutions

Objects of interest occupying minority classes, therefore, receive more significance and see improved accuracy. Object detection is customarily considered to be much harder than image classification, particularly because of these five challenges: dual priorities, speed, multiple scales, limited data, and class imbalance. Researchers have dedicated much effort to overcome these difficulties, yielding oftentimes amazing results; however, significant challenges still persist. Basically all object detection frameworks continue to struggle with small objects, especially those bunched together with partial occlusions. Real-time detection with top-level classification and localization accuracy remains challenging, and practitioners must often prioritize one or the other when making design decisions. Video tracking may see improvements in the future if some continuity between frames is assumed rather than processing each frame individually. Furthermore, an interesting enhancement that may see more exploration would extend the current two-dimensional bounding boxes into three-dimensional bounding cubes. Even though many object detection obstacles have seen creative solutions, these additional considerations–and plenty more–signal that object detection research is certainly not done!