Recently, the object detection and recognition based applications are widely adopted in various real-time and offline applications. The computer vision based automatic learning schemes have gained huge attraction from researchers due to their significant nature of learning that can significantly improve the detection performance. The advances in deep and convolutional neural networks have improved the efficiency of applications based on recognition and detection. However, enhancing precision, decreasing detection error, and detecting camouflaged items are still regarded as difficult problems. In this work, we concentrated on these problems and presented a model based on Faster-RCNN that utilizes saliency detection, proposal generation and bounding box regression, for better detection along with loss functions. The suggested method is referred to as the saliency driven Faster RCNN model for object detection and recognition using computer vision approach (SGFr-RCNN). The performance of the suggested strategy is assessed using the data sets (PASCAL VOC 2007, PASCAL VOC 2012 & CAMO_UOW) and contrasted with current methods in terms of mean average precision. The comparative research demonstrates the important improvement in the results of the suggested strategy relative to the current methods.