Remote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG) optimization technique to increase the exploitation in feature selection. Augmentation is applied in input images to generate more images, and this helps to train the model and reduces data imbalance problems. The VGG-19 and ResNet50 model is applied for feature extraction, and this helps to extract deep features to represent objects. The SSG feature selection technique increases the exploitation and select unique features for object detection that helps to overcome the data imbalance and overfitting problem. The SSG feature selection model helps to balance the exploration and exploitation that escape from the local optima trap. The SSG model has 82.45% mAP, the SSD model has 52.6% mAP, and the MPFP-Net model has 80.43% mAP.