Object detection is crucial in diverse autonomous systems like surveillance, autonomous driving, and driver assistance, ensuring safety by recognizing pedestrians, vehicles, traffic lights, and signs. However, adverse weather conditions such as snow, fog, and rain pose a challenge, affecting detection accuracy and risking accidents and damage. This clearly demonstrates the need for robust object detection solutions that work in all weather conditions. We employed three strategies to enhance deep learningbased object detection in adverse weather: training on real world all-weather images, training on images with synthetic augmented weather noise, and integrating object detection with adverse weather image denoising. The synthetic weather noise is generated using analytical methods, GAN networks, and style-transfer networks. We compared the performance of these strategies by training object detection models using real-world all-weather images from the BDD100K dataset and, for assessment, employed unseen real-world adverse weather images. Adverse weather denoising methods were evaluated by denoising real-world adverse weather images, and the results of object detection denoised and original noisy images were compared. We found that the model trained using all-weather real-world images performed best, while the strategy of doing object detection on denoised images performed worst.