The worst environmental issue of this decade is pollution resulting from marine plastics that significantly affects the ecosystems, marine life, human health, and the economy. In order for this problem to be solved, it becomes compulsory to prevent plastic waste from reaching marine waters through the correct identification and removal of the material from most likely locations on the land. In the past few years, rapid progress has been made in the domain of computer vision and deep learning which has shown strong potential to handle this challenge but hurdles like object recognition in complex scenarios, high preciseness maintenance, and rapid detection still persists. This notable void has motivated us to choose this topic for our research whose aim is to overcome these hurdles stated previously in the literature. This study introduces a novel algorithm based on deep learning which when integrated in underwater drones will be used for detection of floating waste which includes small plastic debris and other debris in inland waterbodies and by using various simulation tools, we have seen that our proposed algorithm successfully overcomes the stated voids. The proposed algorithm is using the FloW dataset, which comprises of two main components: the vision based sub-dataset (FloW-Img) and multimodal sub-dataset (FloW-RI). The algorithm demonstrates improved performance in precisely recognising small objects, such as plastic debris, against complex background. It also has unparalleled accuracy and recall despite challenging environmental situations that make the results functional for real-world use. In addition to it the algorithm is optimized for speed, enabling real-time monitoring and timely responses to pollution events. This study makes a remarkable progress in the enhancement of autonomous waste detection and cleanup models.